# Core ## gymnasium.Env ```{eval-rst} .. autofunction:: gymnasium.Env.step ``` ```{eval-rst} .. autofunction:: gymnasium.Env.reset ``` ```{eval-rst} .. autofunction:: gymnasium.Env.render ``` ### Attributes ```{eval-rst} .. autoattribute:: gymnasium.Env.action_space This attribute gives the format of valid actions. It is of datatype `Space` provided by Gymnasium. For example, if the action space is of type `Discrete` and gives the value `Discrete(2)`, this means there are two valid discrete actions: 0 & 1. .. code:: >>> env.action_space Discrete(2) >>> env.observation_space Box(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32) ``` ```{eval-rst} .. autoattribute:: gymnasium.Env.observation_space This attribute gives the format of valid observations. It is of datatype :class:`Space` provided by Gymnasium. For example, if the observation space is of type :class:`Box` and the shape of the object is ``(4,)``, this denotes a valid observation will be an array of 4 numbers. We can check the box bounds as well with attributes. .. code:: >>> env.observation_space.high array([4.8000002e+00, 3.4028235e+38, 4.1887903e-01, 3.4028235e+38], dtype=float32) >>> env.observation_space.low array([-4.8000002e+00, -3.4028235e+38, -4.1887903e-01, -3.4028235e+38], dtype=float32) ``` ```{eval-rst} .. autoattribute:: gymnasium.Env.reward_range This attribute is a tuple corresponding to min and max possible rewards. Default range is set to ``(-inf,+inf)``. You can set it if you want a narrower range. ``` ### Additional Methods ```{eval-rst} .. autofunction:: gymnasium.Env.close ``` ```{eval-rst} .. autofunction:: gymnasium.Env.seed ``` ## gymnasium.Wrapper ```{eval-rst} .. autoclass:: gymnasium.Wrapper ``` ## gymnasium.ObservationWrapper ```{eval-rst} .. autoclass:: gymnasium.ObservationWrapper ``` ## gymnasium.RewardWrapper ```{eval-rst} .. autoclass:: gymnasium.RewardWrapper ``` ## gymnasium.ActionWrapper ```{eval-rst} .. autoclass:: gymnasium.ActionWrapper ```