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<p class="caption" role="heading"><span class="caption-text">Introduction</span></p>
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<section id="make-your-own-custom-environment">
<h1>Make your own custom environment<a class="headerlink" href="#make-your-own-custom-environment" title="Permalink to this heading">#</a></h1>
<p>This documentation overviews creating new environments and relevant
useful wrappers, utilities and tests included in Gymnasium designed for
the creation of new environments. You can clone gym-examples to play
with the code that is presented here. We recommend that you use a virtual environment:</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">git clone https://github.com/Farama-Foundation/gym-examples</span>
<span class="go">cd gym-examples</span>
<span class="go">python -m venv .env</span>
<span class="go">source .env/bin/activate</span>
<span class="go">pip install -e .</span>
</pre></div>
</div>
<section id="subclassing-gymnasium-env">
<h2>Subclassing gymnasium.Env<a class="headerlink" href="#subclassing-gymnasium-env" title="Permalink to this heading">#</a></h2>
<p>Before learning how to create your own environment you should check out
<a class="reference external" href="/api/core">the documentation of Gymnasium’s API</a>.</p>
<p>We will be concerned with a subset of gym-examples that looks like this:</p>
<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>gym-examples/
README.md
setup.py
gym_examples/
__init__.py
envs/
__init__.py
grid_world.py
wrappers/
__init__.py
relative_position.py
reacher_weighted_reward.py
discrete_action.py
clip_reward.py
</pre></div>
</div>
<p>To illustrate the process of subclassing <code class="docutils literal notranslate"><span class="pre">gymnasium.Env</span></code>, we will
implement a very simplistic game, called <code class="docutils literal notranslate"><span class="pre">GridWorldEnv</span></code>. We will write
the code for our custom environment in
<code class="docutils literal notranslate"><span class="pre">gym-examples/gym_examples/envs/grid_world.py</span></code>. The environment
consists of a 2-dimensional square grid of fixed size (specified via the
<code class="docutils literal notranslate"><span class="pre">size</span></code> parameter during construction). The agent can move vertically
or horizontally between grid cells in each timestep. The goal of the
agent is to navigate to a target on the grid that has been placed
randomly at the beginning of the episode.</p>
<ul class="simple">
<li><p>Observations provide the location of the target and agent.</p></li>
<li><p>There are 4 actions in our environment, corresponding to the
movements “right”, “up”, “left”, and “down”.</p></li>
<li><p>A done signal is issued as soon as the agent has navigated to the
grid cell where the target is located.</p></li>
<li><p>Rewards are binary and sparse, meaning that the immediate reward is
always zero, unless the agent has reached the target, then it is 1.</p></li>
</ul>
<p>An episode in this environment (with <code class="docutils literal notranslate"><span class="pre">size=5</span></code>) might look like this:</p>
<p>where the blue dot is the agent and the red square represents the
target.</p>
<p>Let us look at the source code of <code class="docutils literal notranslate"><span class="pre">GridWorldEnv</span></code> piece by piece:</p>
<section id="declaration-and-initialization">
<h3>Declaration and Initialization<a class="headerlink" href="#declaration-and-initialization" title="Permalink to this heading">#</a></h3>
<p>Our custom environment will inherit from the abstract class
<code class="docutils literal notranslate"><span class="pre">gymnasium.Env</span></code>. You shouldn’t forget to add the <code class="docutils literal notranslate"><span class="pre">metadata</span></code>
attribute to your class. There, you should specify the render-modes that
are supported by your environment (e.g. <code class="docutils literal notranslate"><span class="pre">&quot;human&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;rgb_array&quot;</span></code>,
<code class="docutils literal notranslate"><span class="pre">&quot;ansi&quot;</span></code>) and the framerate at which your environment should be
rendered. Every environment should support <code class="docutils literal notranslate"><span class="pre">None</span></code> as render-mode; you
don’t need to add it in the metadata. In <code class="docutils literal notranslate"><span class="pre">GridWorldEnv</span></code>, we will
support the modes “rgb_array” and “human” and render at 4 FPS.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">__init__</span></code> method of our environment will accept the integer
<code class="docutils literal notranslate"><span class="pre">size</span></code>, that determines the size of the square grid. We will set up
some variables for rendering and define <code class="docutils literal notranslate"><span class="pre">self.observation_space</span></code> and
<code class="docutils literal notranslate"><span class="pre">self.action_space</span></code>. In our case, observations should provide
information about the location of the agent and target on the
2-dimensional grid. We will choose to represent observations in the form
of dictionaries with keys <code class="docutils literal notranslate"><span class="pre">&quot;agent&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;target&quot;</span></code>. An observation
may look like <code class="docutils literal notranslate"><span class="pre">{&quot;agent&quot;:</span> <span class="pre">array([1,</span> <span class="pre">0]),</span> <span class="pre">&quot;target&quot;:</span> <span class="pre">array([0,</span> <span class="pre">3])}</span></code>.
Since we have 4 actions in our environment (“right”, “up”, “left”,
“down”), we will use <code class="docutils literal notranslate"><span class="pre">Discrete(4)</span></code> as an action space. Here is the
declaration of <code class="docutils literal notranslate"><span class="pre">GridWorldEnv</span></code> and the implementation of <code class="docutils literal notranslate"><span class="pre">__init__</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pygame</span>
<span class="kn">import</span> <span class="nn">gymnasium</span> <span class="k">as</span> <span class="nn">gym</span>
<span class="kn">from</span> <span class="nn">gymnasium</span> <span class="kn">import</span> <span class="n">spaces</span>
<span class="k">class</span> <span class="nc">GridWorldEnv</span><span class="p">(</span><span class="n">gym</span><span class="o">.</span><span class="n">Env</span><span class="p">):</span>
<span class="n">metadata</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;render_modes&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;human&quot;</span><span class="p">,</span> <span class="s2">&quot;rgb_array&quot;</span><span class="p">],</span> <span class="s2">&quot;render_fps&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">}</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">render_mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> <span class="c1"># The size of the square grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">=</span> <span class="mi">512</span> <span class="c1"># The size of the PyGame window</span>
<span class="c1"># Observations are dictionaries with the agent&#39;s and the target&#39;s location.</span>
<span class="c1"># Each location is encoded as an element of {0, ..., `size`}^2, i.e. MultiDiscrete([size, size]).</span>
<span class="bp">self</span><span class="o">.</span><span class="n">observation_space</span> <span class="o">=</span> <span class="n">spaces</span><span class="o">.</span><span class="n">Dict</span><span class="p">(</span>
<span class="p">{</span>
<span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="n">spaces</span><span class="o">.</span><span class="n">Box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">),</span>
<span class="s2">&quot;target&quot;</span><span class="p">:</span> <span class="n">spaces</span><span class="o">.</span><span class="n">Box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">),</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="c1"># We have 4 actions, corresponding to &quot;right&quot;, &quot;up&quot;, &quot;left&quot;, &quot;down&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">action_space</span> <span class="o">=</span> <span class="n">spaces</span><span class="o">.</span><span class="n">Discrete</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The following dictionary maps abstract actions from `self.action_space` to</span>
<span class="sd"> the direction we will walk in if that action is taken.</span>
<span class="sd"> I.e. 0 corresponds to &quot;right&quot;, 1 to &quot;up&quot; etc.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_action_to_direction</span> <span class="o">=</span> <span class="p">{</span>
<span class="mi">0</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
<span class="mi">1</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
<span class="mi">2</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
<span class="mi">3</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
<span class="p">}</span>
<span class="k">assert</span> <span class="n">render_mode</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">render_mode</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;render_modes&quot;</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">=</span> <span class="n">render_mode</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> If human-rendering is used, `self.window` will be a reference</span>
<span class="sd"> to the window that we draw to. `self.clock` will be a clock that is used</span>
<span class="sd"> to ensure that the environment is rendered at the correct framerate in</span>
<span class="sd"> human-mode. They will remain `None` until human-mode is used for the</span>
<span class="sd"> first time.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">clock</span> <span class="o">=</span> <span class="kc">None</span>
<span class="c1"># %%</span>
<span class="c1"># Constructing Observations From Environment States</span>
<span class="c1"># ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</span>
<span class="c1">#</span>
<span class="c1"># Since we will need to compute observations both in ``reset`` and</span>
<span class="c1"># ``step``, it is often convenient to have a (private) method ``_get_obs``</span>
<span class="c1"># that translates the environment’s state into an observation. However,</span>
<span class="c1"># this is not mandatory and you may as well compute observations in</span>
<span class="c1"># ``reset`` and ``step`` separately:</span>
<span class="k">def</span> <span class="nf">_get_obs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span><span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span><span class="p">,</span> <span class="s2">&quot;target&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span><span class="p">}</span>
<span class="c1"># %%</span>
<span class="c1"># We can also implement a similar method for the auxiliary information</span>
<span class="c1"># that is returned by ``step`` and ``reset``. In our case, we would like</span>
<span class="c1"># to provide the manhattan distance between the agent and the target:</span>
<span class="k">def</span> <span class="nf">_get_info</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s2">&quot;distance&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span><span class="p">,</span> <span class="nb">ord</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
<span class="p">}</span>
<span class="c1"># %%</span>
<span class="c1"># Oftentimes, info will also contain some data that is only available</span>
<span class="c1"># inside the ``step`` method (e.g. individual reward terms). In that case,</span>
<span class="c1"># we would have to update the dictionary that is returned by ``_get_info``</span>
<span class="c1"># in ``step``.</span>
<span class="c1"># %%</span>
<span class="c1"># Reset</span>
<span class="c1"># ~~~~~</span>
<span class="c1">#</span>
<span class="c1"># The ``reset`` method will be called to initiate a new episode. You may</span>
<span class="c1"># assume that the ``step`` method will not be called before ``reset`` has</span>
<span class="c1"># been called. Moreover, ``reset`` should be called whenever a done signal</span>
<span class="c1"># has been issued. Users may pass the ``seed`` keyword to ``reset`` to</span>
<span class="c1"># initialize any random number generator that is used by the environment</span>
<span class="c1"># to a deterministic state. It is recommended to use the random number</span>
<span class="c1"># generator ``self.np_random`` that is provided by the environment’s base</span>
<span class="c1"># class, ``gymnasium.Env``. If you only use this RNG, you do not need to</span>
<span class="c1"># worry much about seeding, *but you need to remember to call</span>
<span class="c1"># ``super().reset(seed=seed)``* to make sure that ``gymnasium.Env``</span>
<span class="c1"># correctly seeds the RNG. Once this is done, we can randomly set the</span>
<span class="c1"># state of our environment. In our case, we randomly choose the agent’s</span>
<span class="c1"># location and the random sample target positions, until it does not</span>
<span class="c1"># coincide with the agent’s position.</span>
<span class="c1">#</span>
<span class="c1"># The ``reset`` method should return a tuple of the initial observation</span>
<span class="c1"># and some auxiliary information. We can use the methods ``_get_obs`` and</span>
<span class="c1"># ``_get_info`` that we implemented earlier for that:</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># We need the following line to seed self.np_random</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">reset</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
<span class="c1"># Choose the agent&#39;s location uniformly at random</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">np_random</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
<span class="c1"># We will sample the target&#39;s location randomly until it does not coincide with the agent&#39;s location</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span>
<span class="k">while</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">np_random</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span>
<span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span>
<span class="p">)</span>
<span class="n">observation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obs</span><span class="p">()</span>
<span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_info</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;human&quot;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_render_frame</span><span class="p">()</span>
<span class="k">return</span> <span class="n">observation</span><span class="p">,</span> <span class="n">info</span>
<span class="c1"># %%</span>
<span class="c1"># Step</span>
<span class="c1"># ~~~~</span>
<span class="c1">#</span>
<span class="c1"># The ``step`` method usually contains most of the logic of your</span>
<span class="c1"># environment. It accepts an ``action``, computes the state of the</span>
<span class="c1"># environment after applying that action and returns the 4-tuple</span>
<span class="c1"># ``(observation, reward, done, info)``. Once the new state of the</span>
<span class="c1"># environment has been computed, we can check whether it is a terminal</span>
<span class="c1"># state and we set ``done`` accordingly. Since we are using sparse binary</span>
<span class="c1"># rewards in ``GridWorldEnv``, computing ``reward`` is trivial once we</span>
<span class="c1"># know ``done``. To gather ``observation`` and ``info``, we can again make</span>
<span class="c1"># use of ``_get_obs`` and ``_get_info``:</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">action</span><span class="p">):</span>
<span class="c1"># Map the action (element of {0,1,2,3}) to the direction we walk in</span>
<span class="n">direction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_action_to_direction</span><span class="p">[</span><span class="n">action</span><span class="p">]</span>
<span class="c1"># We use `np.clip` to make sure we don&#39;t leave the grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span> <span class="o">+</span> <span class="n">direction</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="mi">1</span>
<span class="p">)</span>
<span class="c1"># An episode is done iff the agent has reached the target</span>
<span class="n">terminated</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span><span class="p">)</span>
<span class="n">reward</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">terminated</span> <span class="k">else</span> <span class="mi">0</span> <span class="c1"># Binary sparse rewards</span>
<span class="n">observation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_obs</span><span class="p">()</span>
<span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_info</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;human&quot;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_render_frame</span><span class="p">()</span>
<span class="k">return</span> <span class="n">observation</span><span class="p">,</span> <span class="n">reward</span><span class="p">,</span> <span class="n">terminated</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">info</span>
<span class="c1"># %%</span>
<span class="c1"># Rendering</span>
<span class="c1"># ~~~~~~~~~</span>
<span class="c1">#</span>
<span class="c1"># Here, we are using PyGame for rendering. A similar approach to rendering</span>
<span class="c1"># is used in many environments that are included with Gymnasium and you</span>
<span class="c1"># can use it as a skeleton for your own environments:</span>
<span class="k">def</span> <span class="nf">render</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;rgb_array&quot;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_render_frame</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_render_frame</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;human&quot;</span><span class="p">:</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">init</span><span class="p">()</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">init</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="n">pygame</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">set_mode</span><span class="p">(</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">clock</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;human&quot;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">clock</span> <span class="o">=</span> <span class="n">pygame</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">Clock</span><span class="p">()</span>
<span class="n">canvas</span> <span class="o">=</span> <span class="n">pygame</span><span class="o">.</span><span class="n">Surface</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">))</span>
<span class="n">canvas</span><span class="o">.</span><span class="n">fill</span><span class="p">((</span><span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">))</span>
<span class="n">pix_square_size</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">window_size</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span>
<span class="p">)</span> <span class="c1"># The size of a single grid square in pixels</span>
<span class="c1"># First we draw the target</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">draw</span><span class="o">.</span><span class="n">rect</span><span class="p">(</span>
<span class="n">canvas</span><span class="p">,</span>
<span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">Rect</span><span class="p">(</span>
<span class="n">pix_square_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_location</span><span class="p">,</span>
<span class="p">(</span><span class="n">pix_square_size</span><span class="p">,</span> <span class="n">pix_square_size</span><span class="p">),</span>
<span class="p">),</span>
<span class="p">)</span>
<span class="c1"># Now we draw the agent</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">draw</span><span class="o">.</span><span class="n">circle</span><span class="p">(</span>
<span class="n">canvas</span><span class="p">,</span>
<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">),</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_agent_location</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="n">pix_square_size</span><span class="p">,</span>
<span class="n">pix_square_size</span> <span class="o">/</span> <span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># Finally, add some gridlines</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">draw</span><span class="o">.</span><span class="n">line</span><span class="p">(</span>
<span class="n">canvas</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">pix_square_size</span> <span class="o">*</span> <span class="n">x</span><span class="p">),</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">,</span> <span class="n">pix_square_size</span> <span class="o">*</span> <span class="n">x</span><span class="p">),</span>
<span class="n">width</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">draw</span><span class="o">.</span><span class="n">line</span><span class="p">(</span>
<span class="n">canvas</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="p">(</span><span class="n">pix_square_size</span> <span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
<span class="p">(</span><span class="n">pix_square_size</span> <span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_size</span><span class="p">),</span>
<span class="n">width</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">render_mode</span> <span class="o">==</span> <span class="s2">&quot;human&quot;</span><span class="p">:</span>
<span class="c1"># The following line copies our drawings from `canvas` to the visible window</span>
<span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="o">.</span><span class="n">blit</span><span class="p">(</span><span class="n">canvas</span><span class="p">,</span> <span class="n">canvas</span><span class="o">.</span><span class="n">get_rect</span><span class="p">())</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">pump</span><span class="p">()</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
<span class="c1"># We need to ensure that human-rendering occurs at the predefined framerate.</span>
<span class="c1"># The following line will automatically add a delay to keep the framerate stable.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">tick</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;render_fps&quot;</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># rgb_array</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span>
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pygame</span><span class="o">.</span><span class="n">surfarray</span><span class="o">.</span><span class="n">pixels3d</span><span class="p">(</span><span class="n">canvas</span><span class="p">)),</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="p">)</span>
<span class="c1"># %%</span>
<span class="c1"># Close</span>
<span class="c1"># ~~~~~</span>
<span class="c1">#</span>
<span class="c1"># The ``close`` method should close any open resources that were used by</span>
<span class="c1"># the environment. In many cases, you don’t actually have to bother to</span>
<span class="c1"># implement this method. However, in our example ``render_mode`` may be</span>
<span class="c1"># ``&quot;human&quot;`` and we might need to close the window that has been opened:</span>
<span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">pygame</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
</pre></div>
</div>
<p>In other environments <code class="docutils literal notranslate"><span class="pre">close</span></code> might also close files that were opened
or release other resources. You shouldn’t interact with the environment
after having called <code class="docutils literal notranslate"><span class="pre">close</span></code>.</p>
</section>
</section>
<section id="registering-envs">
<h2>Registering Envs<a class="headerlink" href="#registering-envs" title="Permalink to this heading">#</a></h2>
<p>In order for the custom environments to be detected by Gymnasium, they
must be registered as follows. We will choose to put this code in
<code class="docutils literal notranslate"><span class="pre">gym-examples/gym_examples/__init__.py</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">gymnasium.envs.registration</span> <span class="kn">import</span> <span class="n">register</span>
<span class="n">register</span><span class="p">(</span>
<span class="nb">id</span><span class="o">=</span><span class="s2">&quot;gym_examples/GridWorld-v0&quot;</span><span class="p">,</span>
<span class="n">entry_point</span><span class="o">=</span><span class="s2">&quot;gym_examples.envs:GridWorldEnv&quot;</span><span class="p">,</span>
<span class="n">max_episode_steps</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>The environment ID consists of three components, two of which are
optional: an optional namespace (here: <code class="docutils literal notranslate"><span class="pre">gym_examples</span></code>), a mandatory
name (here: <code class="docutils literal notranslate"><span class="pre">GridWorld</span></code>) and an optional but recommended version
(here: v0). It might have also been registered as <code class="docutils literal notranslate"><span class="pre">GridWorld-v0</span></code> (the
recommended approach), <code class="docutils literal notranslate"><span class="pre">GridWorld</span></code> or <code class="docutils literal notranslate"><span class="pre">gym_examples/GridWorld</span></code>, and
the appropriate ID should then be used during environment creation.</p>
<p>The keyword argument <code class="docutils literal notranslate"><span class="pre">max_episode_steps=300</span></code> will ensure that
GridWorld environments that are instantiated via <code class="docutils literal notranslate"><span class="pre">gymnasium.make</span></code> will
be wrapped in a <code class="docutils literal notranslate"><span class="pre">TimeLimit</span></code> wrapper (see <a class="reference external" href="/api/wrappers">the wrapper
documentation</a> for more information). A done signal
will then be produced if the agent has reached the target <em>or</em> 300 steps
have been executed in the current episode. To distinguish truncation and
termination, you can check <code class="docutils literal notranslate"><span class="pre">info[&quot;TimeLimit.truncated&quot;]</span></code>.</p>
<p>Apart from <code class="docutils literal notranslate"><span class="pre">id</span></code> and <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code>, you may pass the following
additional keyword arguments to <code class="docutils literal notranslate"><span class="pre">register</span></code>:</p>
<div class="table-wrapper docutils container">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Name</p></th>
<th class="head"><p>Type</p></th>
<th class="head"><p>Default</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">reward_threshold</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">float</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">None</span></code></p></td>
<td><p>The reward threshold before the task is considered solved</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">nondeterministic</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">False</span></code></p></td>
<td><p>Whether this environment is non-deterministic even after seeding</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">max_episode_steps</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">None</span></code></p></td>
<td><p>The maximum number of steps that an episode can consist of. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, a <code class="docutils literal notranslate"><span class="pre">TimeLimit</span></code> wrapper is added</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">order_enforce</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">True</span></code></p></td>
<td><p>Whether to wrap the environment in an <code class="docutils literal notranslate"><span class="pre">OrderEnforcing</span></code> wrapper</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">autoreset</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">bool</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">False</span></code></p></td>
<td><p>Whether to wrap the environment in an <code class="docutils literal notranslate"><span class="pre">AutoResetWrapper</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">kwargs</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">dict</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">{}</span></code></p></td>
<td><p>The default kwargs to pass to the environment class</p></td>
</tr>
</tbody>
</table>
</div>
<p>Most of these keywords (except for <code class="docutils literal notranslate"><span class="pre">max_episode_steps</span></code>,
<code class="docutils literal notranslate"><span class="pre">order_enforce</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>) do not alter the behavior of
environment instances but merely provide some extra information about
your environment. After registration, our custom <code class="docutils literal notranslate"><span class="pre">GridWorldEnv</span></code>
environment can be created with
<code class="docutils literal notranslate"><span class="pre">env</span> <span class="pre">=</span> <span class="pre">gymnasium.make('gym_examples/GridWorld-v0')</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">gym-examples/gym_examples/envs/__init__.py</span></code> should have:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">gym_examples.envs.grid_world</span> <span class="kn">import</span> <span class="n">GridWorldEnv</span>
</pre></div>
</div>
<p>If your environment is not registered, you may optionally pass a module
to import, that would register your environment before creating it like
this - <code class="docutils literal notranslate"><span class="pre">env</span> <span class="pre">=</span> <span class="pre">gymnasium.make('module:Env-v0')</span></code>, where <code class="docutils literal notranslate"><span class="pre">module</span></code>
contains the registration code. For the GridWorld env, the registration
code is run by importing <code class="docutils literal notranslate"><span class="pre">gym_examples</span></code> so if it were not possible to
import gym_examples explicitly, you could register while making by
<code class="docutils literal notranslate"><span class="pre">env</span> <span class="pre">=</span> <span class="pre">gymnasium.make('gym_examples:gym_examples/GridWorld-v0)</span></code>. This
is especially useful when you’re allowed to pass only the environment ID
into a third-party codebase (eg. learning library). This lets you
register your environment without needing to edit the library’s source
code.</p>
</section>
<section id="creating-a-package">
<h2>Creating a Package<a class="headerlink" href="#creating-a-package" title="Permalink to this heading">#</a></h2>
<p>The last step is to structure our code as a Python package. This
involves configuring <code class="docutils literal notranslate"><span class="pre">gym-examples/setup.py</span></code>. A minimal example of how
to do so is as follows:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="kn">import</span> <span class="n">setup</span>
<span class="n">setup</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">&quot;gym_examples&quot;</span><span class="p">,</span>
<span class="n">version</span><span class="o">=</span><span class="s2">&quot;0.0.1&quot;</span><span class="p">,</span>
<span class="n">install_requires</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gymnasium==0.26.0&quot;</span><span class="p">,</span> <span class="s2">&quot;pygame==2.1.0&quot;</span><span class="p">],</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="creating-environment-instances">
<h2>Creating Environment Instances<a class="headerlink" href="#creating-environment-instances" title="Permalink to this heading">#</a></h2>
<p>After you have installed your package locally with
<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">-e</span> <span class="pre">gym-examples</span></code>, you can create an instance of the
environment via:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym_examples</span>
<span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">&#39;gym_examples/GridWorld-v0&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>You can also pass keyword arguments of your environment’s constructor to
<code class="docutils literal notranslate"><span class="pre">gymnasium.make</span></code> to customize the environment. In our case, we could
do:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">&#39;gym_examples/GridWorld-v0&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>Sometimes, you may find it more convenient to skip registration and call
the environment’s constructor yourself. Some may find this approach more
pythonic and environments that are instantiated like this are also
perfectly fine (but remember to add wrappers as well!).</p>
</section>
<section id="using-wrappers">
<h2>Using Wrappers<a class="headerlink" href="#using-wrappers" title="Permalink to this heading">#</a></h2>
<p>Oftentimes, we want to use different variants of a custom environment,
or we want to modify the behavior of an environment that is provided by
Gymnasium or some other party. Wrappers allow us to do this without
changing the environment implementation or adding any boilerplate code.
Check out the <a class="reference external" href="/api/wrappers/">wrapper documentation</a> for details on
how to use wrappers and instructions for implementing your own. In our
example, observations cannot be used directly in learning code because
they are dictionaries. However, we don’t actually need to touch our
environment implementation to fix this! We can simply add a wrapper on
top of environment instances to flatten observations into a single
array:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym_examples</span>
<span class="kn">from</span> <span class="nn">gymnasium.wrappers</span> <span class="kn">import</span> <span class="n">FlattenObservation</span>
<span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">&#39;gym_examples/GridWorld-v0&#39;</span><span class="p">)</span>
<span class="n">wrapped_env</span> <span class="o">=</span> <span class="n">FlattenObservation</span><span class="p">(</span><span class="n">env</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">wrapped_env</span><span class="o">.</span><span class="n">reset</span><span class="p">())</span> <span class="c1"># E.g. [3 0 3 3], {}</span>
</pre></div>
</div>
<p>Wrappers have the big advantage that they make environments highly
modular. For instance, instead of flattening the observations from
GridWorld, you might only want to look at the relative position of the
target and the agent. In the section on
<a class="reference external" href="/api/wrappers/#observationwrapper">ObservationWrappers</a> we have
implemented a wrapper that does this job. This wrapper is also available
in gym-examples:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym_examples</span>
<span class="kn">from</span> <span class="nn">gym_examples.wrappers</span> <span class="kn">import</span> <span class="n">RelativePosition</span>
<span class="n">env</span> <span class="o">=</span> <span class="n">gymnasium</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">&#39;gym_examples/GridWorld-v0&#39;</span><span class="p">)</span>
<span class="n">wrapped_env</span> <span class="o">=</span> <span class="n">RelativePosition</span><span class="p">(</span><span class="n">env</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">wrapped_env</span><span class="o">.</span><span class="n">reset</span><span class="p">())</span> <span class="c1"># E.g. [-3 3], {}</span>
</pre></div>
</div>
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<li><a class="reference internal" href="#">Make your own custom environment</a><ul>
<li><a class="reference internal" href="#subclassing-gymnasium-env">Subclassing gymnasium.Env</a><ul>
<li><a class="reference internal" href="#declaration-and-initialization">Declaration and Initialization</a></li>
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<li><a class="reference internal" href="#registering-envs">Registering Envs</a></li>
<li><a class="reference internal" href="#creating-a-package">Creating a Package</a></li>
<li><a class="reference internal" href="#creating-environment-instances">Creating Environment Instances</a></li>
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