6c6c872523
Changes: - Disclaimer in README - Replaced all occurences of Gym with Gymnasium - Removed code that is now dead since we no longer need to support the old step API - Updated type hints to only allow new step API - Increased required version of envpool to support Gymnasium - Increased required version of PettingZoo to support Gymnasium - Updated `PettingZooEnv` to only use the new step API, removed hack to also support old API - I had to add some `# type: ignore` comments, due to new type hinting in Gymnasium. I'm not that familiar with type hinting but I believe that the issue is on the Gymnasium side and we are looking into it. - Had to update `MyTestEnv` to support `options` kwarg - Skip NNI tests because they still use OpenAI Gym - Also allow `PettingZooEnv` in vector environment - Updated doc page about ReplayBuffer to also talk about terminated and truncated flags. Still need to do: - Update the Jupyter notebooks in docs - Check the entire code base for more dead code (from compatibility stuff) - Check the reset functions of all environments/wrappers in code base to make sure they use the `options` kwarg - Someone might want to check test_env_finite.py - Is it okay to allow `PettingZooEnv` in vector environments? Might need to update docs?
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import sys
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import time
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import gymnasium as gym
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import numpy as np
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import tqdm
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from tianshou.data import Batch, ReplayBuffer, VectorReplayBuffer
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def test_replaybuffer(task="Pendulum-v1"):
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total_count = 5
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for _ in tqdm.trange(total_count, desc="ReplayBuffer"):
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env = gym.make(task)
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buf = ReplayBuffer(10000)
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obs, info = env.reset()
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for _ in range(100000):
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act = env.action_space.sample()
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obs_next, rew, terminated, truncated, info = env.step(act)
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done = terminated or truncated
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batch = Batch(
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obs=np.array([obs]),
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act=np.array([act]),
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rew=np.array([rew]),
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terminated=np.array([terminated]),
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truncated=np.array([truncated]),
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done=np.array([done]),
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obs_next=np.array([obs_next]),
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info=np.array([info]),
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)
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buf.add(batch, buffer_ids=[0])
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obs = obs_next
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if done:
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obs, info = env.reset()
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def test_vectorbuffer(task="Pendulum-v1"):
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total_count = 5
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for _ in tqdm.trange(total_count, desc="VectorReplayBuffer"):
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env = gym.make(task)
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buf = VectorReplayBuffer(total_size=10000, buffer_num=1)
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obs, info = env.reset()
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for _ in range(100000):
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act = env.action_space.sample()
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obs_next, rew, terminated, truncated, info = env.step(act)
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done = terminated or truncated
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batch = Batch(
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obs=np.array([obs]),
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act=np.array([act]),
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rew=np.array([rew]),
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terminated=np.array([terminated]),
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truncated=np.array([truncated]),
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obs_next=np.array([obs_next]),
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info=np.array([info]),
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)
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buf.add(batch)
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obs = obs_next
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if done:
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obs, info = env.reset()
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if __name__ == '__main__':
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t0 = time.time()
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test_replaybuffer(sys.argv[-1])
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print("test replaybuffer: ", time.time() - t0)
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t0 = time.time()
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test_vectorbuffer(sys.argv[-1])
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print("test vectorbuffer: ", time.time() - t0)
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