85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
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import gym
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import numpy as np
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from fancy_gym import make
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def run_env(env_id, iterations=None, seed=SEED, render=False):
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"""
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Example for running a DMC based env in the step based setting.
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The env_id has to be specified as `domain_name-task_name` or
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for manipulation tasks as `manipulation-environment_name`
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Args:
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env_id: Either `domain_name-task_name` or `manipulation-environment_name`
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iterations: Number of rollout steps to run
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seed= random seeding
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render: Render the episode
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Returns:
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"""
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env: gym.Env = make(env_id, seed=seed)
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rewards = []
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observations = []
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dones = []
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obs = env.reset()
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_verify_observations(obs, env.observation_space, "reset()")
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length = env.spec.max_episode_steps
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if iterations is None:
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if length is None:
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iterations = 1
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else:
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iterations = length
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# number of samples(multiple environment steps)
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for i in range(iterations):
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observations.append(obs)
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ac = env.action_space.sample()
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# ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
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obs, reward, done, info = env.step(ac)
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_verify_observations(obs, env.observation_space, "step()")
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_verify_reward(reward)
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_verify_done(done)
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rewards.append(reward)
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dones.append(done)
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if render:
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env.render("human")
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if done:
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obs = env.reset()
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assert done, "Done flag is not True after max episode length."
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observations.append(obs)
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env.close()
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del env
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return np.array(observations), np.array(rewards), np.array(dones)
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def _run_env_determinism(self, env_id: str, seed: int):
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traj1 = self.run_env(env_id, seed=seed)
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traj2 = self.run_env(env_id, seed=seed)
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# Iterate over two trajectories, which should have the same state and action sequence
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for i, time_step in enumerate(zip(*traj1, *traj2)):
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obs1, rwd1, done1, obs2, rwd2, done2 = time_step
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self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match.")
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self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
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self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
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def _verify_observations(obs, observation_space, obs_type="reset()"):
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assert observation_space.contains(obs), \
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f"Observation {obs} received from {obs_type} not contained in observation space {observation_space}."
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def _verify_reward(reward):
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assert isinstance(reward, float), f"Returned {reward} as reward, expected float."
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def _verify_done(done):
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assert isinstance(done, bool), f"Returned {done} as done flag, expected bool."
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