mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-21 06:20:15 +00:00
d337f4e571
* fix double reset, as suggested by @jietang * better floors and ceilings * add convenience methods to monitor * add wrappers to gym namespace * allow playing Atari games, with potentially more coming in the future * simplify example in docs * Move play out of the Env * fix tests * no more deprecation warnings * remove env.monitor * monitor simplification * monitor simplifications * monitor related fixes * a few changes suggested by linter * timestep_limit fixes * keep track of gym env variables for future compatibility * timestep_limit => max_episode_timesteps * don't apply TimeLimit wrapper in make for VNC envs * Respect old timestep_limit argument * Pass max_episode_seconds through registration * Don't include deprecation warnings yet
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
"""
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This script does a few rollouts with an environment and writes the data to an npz file
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Its purpose is to help with verifying that you haven't functionally changed an environment.
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(If you have, you should bump the version number.)
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"""
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import argparse, numpy as np, collections, sys
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from os import path
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class RandomAgent(object):
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def __init__(self, ac_space):
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self.ac_space = ac_space
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def act(self, _):
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return self.ac_space.sample()
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def rollout(env, agent, max_episode_steps):
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"""
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Simulate the env and agent for max_episode_steps
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"""
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ob = env.reset()
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data = collections.defaultdict(list)
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for _ in xrange(max_episode_steps):
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data["observation"].append(ob)
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action = agent.act(ob)
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data["action"].append(action)
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ob,rew,done,_ = env.step(action)
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data["reward"].append(rew)
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if done:
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break
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return data
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("envid")
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parser.add_argument("outfile")
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parser.add_argument("--gymdir")
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args = parser.parse_args()
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if args.gymdir:
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sys.path.insert(0, args.gymdir)
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import gym
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from gym import utils
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print utils.colorize("gym directory: %s"%path.dirname(gym.__file__), "yellow")
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env = gym.make(args.envid)
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agent = RandomAgent(env.action_space)
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alldata = {}
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for i in xrange(2):
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np.random.seed(i)
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data = rollout(env, agent, env.spec.max_episode_steps)
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for (k, v) in data.items():
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alldata["%i-%s"%(i, k)] = v
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np.savez(args.outfile, **alldata)
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if __name__ == "__main__":
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main()
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