2cc34fb72b
Closes #914 Additional changes: - Deprecate python below 11 - Remove 3rd party and throughput tests. This simplifies install and test pipeline - Remove gym compatibility and shimmy - Format with 3.11 conventions. In particular, add `zip(..., strict=True/False)` where possible Since the additional tests and gym were complicating the CI pipeline (flaky and dist-dependent), it didn't make sense to work on fixing the current tests in this PR to then just delete them in the next one. So this PR changes the build and removes these tests at the same time.
424 lines
15 KiB
Python
424 lines
15 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 pytest
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from gymnasium.spaces.discrete import Discrete
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from tianshou.data import Batch
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from tianshou.env import (
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ContinuousToDiscrete,
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DummyVectorEnv,
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MultiDiscreteToDiscrete,
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RayVectorEnv,
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ShmemVectorEnv,
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SubprocVectorEnv,
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VectorEnvNormObs,
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)
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from tianshou.env.gym_wrappers import TruncatedAsTerminated
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from tianshou.utils import RunningMeanStd
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if __name__ == "__main__":
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from env import MyTestEnv, NXEnv
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else: # pytest
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from test.base.env import MyTestEnv, NXEnv
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try:
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import envpool
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except ImportError:
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envpool = None
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def has_ray():
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try:
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import ray # noqa: F401
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return True
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except ImportError:
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return False
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def recurse_comp(a, b):
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try:
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if isinstance(a, np.ndarray):
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if a.dtype == object:
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return np.array([recurse_comp(m, n) for m, n in zip(a, b, strict=True)]).all()
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return np.allclose(a, b)
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if isinstance(a, list | tuple):
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return np.array([recurse_comp(m, n) for m, n in zip(a, b, strict=True)]).all()
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if isinstance(a, dict):
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return np.array([recurse_comp(a[k], b[k]) for k in a]).all()
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except Exception:
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return False
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def test_async_env(size=10000, num=8, sleep=0.1):
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# simplify the test case, just keep stepping
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env_fns = [
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lambda i=i: MyTestEnv(size=i, sleep=sleep, random_sleep=True)
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for i in range(size, size + num)
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]
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test_cls = [SubprocVectorEnv, ShmemVectorEnv]
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if has_ray():
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test_cls += [RayVectorEnv]
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for cls in test_cls:
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v = cls(env_fns, wait_num=num // 2, timeout=1e-3)
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v.seed(None)
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v.reset()
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# for a random variable u ~ U[0, 1], let v = max{u1, u2, ..., un}
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# P(v <= x) = x^n (0 <= x <= 1), pdf of v is nx^{n-1}
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# expectation of v is n / (n + 1)
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# for a synchronous environment, the following actions should take
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# about 7 * sleep * num / (num + 1) seconds
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# for async simulation, the analysis is complicated, but the time cost
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# should be smaller
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action_list = [1] * num + [0] * (num * 2) + [1] * (num * 4)
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current_idx_start = 0
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act = action_list[:num]
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env_ids = list(range(num))
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o = []
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spent_time = time.time()
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while current_idx_start < len(action_list):
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(
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A,
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B,
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C,
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D,
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E,
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) = v.step(action=act, id=env_ids)
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b = Batch({"obs": A, "rew": B, "terminate": C, "truncated": D, "info": E})
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env_ids = b.info.env_id
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o.append(b)
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current_idx_start += len(act)
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# len of action may be smaller than len(A) in the end
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act = action_list[current_idx_start : current_idx_start + len(A)]
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# truncate env_ids with the first terms
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# typically len(env_ids) == len(A) == len(action), except for the
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# last batch when actions are not enough
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env_ids = env_ids[: len(act)]
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spent_time = time.time() - spent_time
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Batch.cat(o)
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v.close()
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# assure 1/7 improvement
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if sys.platform == "linux" and cls != RayVectorEnv:
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# macOS/Windows cannot pass this check
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assert spent_time < 6.0 * sleep * num / (num + 1)
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def test_async_check_id(size=100, num=4, sleep=0.2, timeout=0.7):
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env_fns = [
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lambda: MyTestEnv(size=size, sleep=sleep * 2),
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lambda: MyTestEnv(size=size, sleep=sleep * 3),
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lambda: MyTestEnv(size=size, sleep=sleep * 5),
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lambda: MyTestEnv(size=size, sleep=sleep * 7),
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]
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test_cls = [SubprocVectorEnv, ShmemVectorEnv]
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if has_ray():
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test_cls += [RayVectorEnv]
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total_pass = 0
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for cls in test_cls:
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pass_check = 1
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v = cls(env_fns, wait_num=num - 1, timeout=timeout)
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t = time.time()
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v.reset()
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t = time.time() - t
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print(f"{cls} reset {t}")
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if t > sleep * 9: # huge than maximum sleep time (7 sleep)
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pass_check = 0
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expect_result = [
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[0, 1],
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[0, 1, 2],
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[0, 1, 3],
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[0, 1, 2],
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[0, 1],
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[0, 2, 3],
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[0, 1],
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]
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ids = np.arange(num)
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for res in expect_result:
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t = time.time()
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_, _, _, _, info = v.step([1] * len(ids), ids)
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t = time.time() - t
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ids = Batch(info).env_id
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print(ids, t)
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if not (
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len(ids) == len(res)
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and np.allclose(sorted(ids), res)
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and (t < timeout) == (len(res) == num - 1)
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):
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pass_check = 0
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break
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total_pass += pass_check
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if sys.platform == "linux": # Windows/macOS may not pass this check
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assert total_pass >= 2
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def test_vecenv(size=10, num=8, sleep=0.001):
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env_fns = [
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lambda i=i: MyTestEnv(size=i, sleep=sleep, recurse_state=True)
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for i in range(size, size + num)
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]
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venv = [
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DummyVectorEnv(env_fns),
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SubprocVectorEnv(env_fns),
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ShmemVectorEnv(env_fns),
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]
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if has_ray() and sys.platform == "linux":
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venv += [RayVectorEnv(env_fns)]
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for v in venv:
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v.seed(0)
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action_list = [1] * 5 + [0] * 10 + [1] * 20
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o = [v.reset()[0] for v in venv]
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for a in action_list:
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o = []
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for v in venv:
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A, B, C, D, E = v.step([a] * num)
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if sum(C + D):
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A, _ = v.reset(np.where(C + D)[0])
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o.append([A, B, C, D, E])
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for index, infos in enumerate(zip(*o, strict=True)):
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if index == 4: # do not check info here
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continue
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for info in infos:
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assert recurse_comp(infos[0], info)
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if __name__ == "__main__":
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t = [0] * len(venv)
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for i, e in enumerate(venv):
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t[i] = time.time()
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e.reset()
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for a in action_list:
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done = e.step([a] * num)[2]
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if sum(done) > 0:
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e.reset(np.where(done)[0])
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t[i] = time.time() - t[i]
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for i, v in enumerate(venv):
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print(f"{type(v)}: {t[i]:.6f}s")
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def assert_get(v, expected):
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assert v.get_env_attr("size") == expected
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assert v.get_env_attr("size", id=0) == [expected[0]]
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assert v.get_env_attr("size", id=[0, 1, 2]) == expected[:3]
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for v in venv:
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assert_get(v, list(range(size, size + num)))
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assert v.env_num == num
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assert v.action_space == [Discrete(2)] * num
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v.set_env_attr("size", 0)
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assert_get(v, [0] * num)
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v.set_env_attr("size", 1, 0)
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assert_get(v, [1] + [0] * (num - 1))
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v.set_env_attr("size", 2, [1, 2, 3])
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assert_get(v, [1] + [2] * 3 + [0] * (num - 4))
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for v in venv:
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v.close()
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def test_attr_unwrapped():
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train_envs = DummyVectorEnv([lambda: gym.make("CartPole-v1")])
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train_envs.set_env_attr("test_attribute", 1337)
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assert train_envs.get_env_attr("test_attribute") == [1337]
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assert hasattr(train_envs.workers[0].env, "test_attribute")
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assert hasattr(train_envs.workers[0].env.unwrapped, "test_attribute")
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def test_env_obs_dtype():
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for obs_type in ["array", "object"]:
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envs = SubprocVectorEnv([lambda i=x, t=obs_type: NXEnv(i, t) for x in [5, 10, 15, 20]])
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obs, info = envs.reset()
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assert obs.dtype == object
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obs = envs.step([1, 1, 1, 1])[0]
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assert obs.dtype == object
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def test_env_reset_optional_kwargs(size=10000, num=8):
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env_fns = [lambda i=i: MyTestEnv(size=i) for i in range(size, size + num)]
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test_cls = [DummyVectorEnv, SubprocVectorEnv, ShmemVectorEnv]
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if has_ray():
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test_cls += [RayVectorEnv]
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for cls in test_cls:
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v = cls(env_fns, wait_num=num // 2, timeout=1e-3)
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_, info = v.reset(seed=1)
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assert len(info) == len(env_fns)
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assert isinstance(info[0], dict)
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def test_venv_wrapper_gym(num_envs: int = 4):
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# Issue 697
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envs = DummyVectorEnv([lambda: gym.make("CartPole-v1") for _ in range(num_envs)])
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envs = VectorEnvNormObs(envs)
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try:
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obs, info = envs.reset()
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except ValueError:
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obs, info = envs.reset(return_info=True)
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assert isinstance(obs, np.ndarray)
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assert isinstance(info, list)
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assert isinstance(info[0], dict)
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assert obs.shape[0] == len(info) == num_envs
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def run_align_norm_obs(raw_env, train_env, test_env, action_list):
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def reset_result_to_obs(reset_result):
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"""Extract observation from reset result (result is possibly a tuple containing info)."""
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if isinstance(reset_result, tuple) and len(reset_result) == 2:
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obs, _ = reset_result
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else:
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obs = reset_result
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return obs
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eps = np.finfo(np.float32).eps.item()
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raw_reset_result = raw_env.reset()
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train_reset_result = train_env.reset()
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initial_raw_obs = reset_result_to_obs(raw_reset_result)
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initial_train_obs = reset_result_to_obs(train_reset_result)
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raw_obs, train_obs = [initial_raw_obs], [initial_train_obs]
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for action in action_list:
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step_result = raw_env.step(action)
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if len(step_result) == 5:
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obs, rew, terminated, truncated, info = step_result
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done = np.logical_or(terminated, truncated)
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else:
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obs, rew, done, info = step_result
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raw_obs.append(obs)
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if np.any(done):
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reset_result = raw_env.reset(np.where(done)[0])
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obs = reset_result_to_obs(reset_result)
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raw_obs.append(obs)
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step_result = train_env.step(action)
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if len(step_result) == 5:
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obs, rew, terminated, truncated, info = step_result
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done = np.logical_or(terminated, truncated)
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else:
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obs, rew, done, info = step_result
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train_obs.append(obs)
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if np.any(done):
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reset_result = train_env.reset(np.where(done)[0])
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obs = reset_result_to_obs(reset_result)
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train_obs.append(obs)
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ref_rms = RunningMeanStd()
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for ro, to in zip(raw_obs, train_obs, strict=True):
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ref_rms.update(ro)
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no = (ro - ref_rms.mean) / np.sqrt(ref_rms.var + eps)
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assert np.allclose(no, to)
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assert np.allclose(ref_rms.mean, train_env.get_obs_rms().mean)
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assert np.allclose(ref_rms.var, train_env.get_obs_rms().var)
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assert np.allclose(ref_rms.mean, test_env.get_obs_rms().mean)
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assert np.allclose(ref_rms.var, test_env.get_obs_rms().var)
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reset_result = test_env.reset()
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obs = reset_result_to_obs(reset_result)
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test_obs = [obs]
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for action in action_list:
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step_result = test_env.step(action)
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if len(step_result) == 5:
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obs, rew, terminated, truncated, info = step_result
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done = np.logical_or(terminated, truncated)
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else:
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obs, rew, done, info = step_result
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test_obs.append(obs)
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if np.any(done):
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reset_result = test_env.reset(np.where(done)[0])
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obs = reset_result_to_obs(reset_result)
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test_obs.append(obs)
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for ro, to in zip(raw_obs, test_obs, strict=True):
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no = (ro - ref_rms.mean) / np.sqrt(ref_rms.var + eps)
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assert np.allclose(no, to)
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def test_venv_norm_obs():
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sizes = np.array([5, 10, 15, 20])
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action = np.array([1, 1, 1, 1])
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total_step = 30
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action_list = [action] * total_step
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env_fns = [lambda i=x: MyTestEnv(size=i, array_state=True) for x in sizes]
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raw = DummyVectorEnv(env_fns)
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train_env = VectorEnvNormObs(DummyVectorEnv(env_fns))
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print(train_env.observation_space)
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test_env = VectorEnvNormObs(DummyVectorEnv(env_fns), update_obs_rms=False)
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test_env.set_obs_rms(train_env.get_obs_rms())
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run_align_norm_obs(raw, train_env, test_env, action_list)
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def test_gym_wrappers():
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class DummyEnv(gym.Env):
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def __init__(self):
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self.action_space = gym.spaces.Box(low=-1.0, high=2.0, shape=(4,), dtype=np.float32)
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self.observation_space = gym.spaces.Discrete(2)
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def step(self, act):
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return self.observation_space.sample(), -1, False, True, {}
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bsz = 10
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action_per_branch = [4, 6, 10, 7]
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env = DummyEnv()
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original_act = env.action_space.high
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# convert continous to multidiscrete action space
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# with different action number per dimension
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env_m = ContinuousToDiscrete(env, action_per_branch)
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# check conversion is working properly for one action
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np.testing.assert_allclose(env_m.action(env_m.action_space.nvec - 1), original_act)
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# check conversion is working properly for a batch of actions
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np.testing.assert_allclose(
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env_m.action(np.array([env_m.action_space.nvec - 1] * bsz)),
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np.array([original_act] * bsz),
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)
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# convert multidiscrete with different action number per
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# dimension to discrete action space
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env_d = MultiDiscreteToDiscrete(env_m)
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# check conversion is working properly for one action
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np.testing.assert_allclose(env_d.action(env_d.action_space.n - 1), env_m.action_space.nvec - 1)
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# check conversion is working properly for a batch of actions
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np.testing.assert_allclose(
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env_d.action(np.array([env_d.action_space.n - 1] * bsz)),
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np.array([env_m.action_space.nvec - 1] * bsz),
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)
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# check truncate is True when terminated
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try:
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env_t = TruncatedAsTerminated(env)
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except OSError:
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env_t = None
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if env_t is not None:
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_, _, truncated, _, _ = env_t.step(env_t.action_space.sample())
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assert truncated
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@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
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def test_venv_wrapper_envpool():
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raw = envpool.make_gymnasium("Ant-v3", num_envs=4)
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train = VectorEnvNormObs(envpool.make_gymnasium("Ant-v3", num_envs=4))
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test = VectorEnvNormObs(envpool.make_gymnasium("Ant-v3", num_envs=4), update_obs_rms=False)
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test.set_obs_rms(train.get_obs_rms())
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actions = [np.array([raw.action_space.sample() for _ in range(4)]) for i in range(30)]
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run_align_norm_obs(raw, train, test, actions)
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@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
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def test_venv_wrapper_envpool_gym_reset_return_info():
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num_envs = 4
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env = VectorEnvNormObs(
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envpool.make_gymnasium("Ant-v3", num_envs=num_envs, gym_reset_return_info=True),
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)
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obs, info = env.reset()
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assert obs.shape[0] == num_envs
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for _, v in info.items():
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if not isinstance(v, dict):
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assert v.shape[0] == num_envs
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if __name__ == "__main__":
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test_venv_norm_obs()
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test_venv_wrapper_gym()
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test_venv_wrapper_envpool()
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test_venv_wrapper_envpool_gym_reset_return_info()
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test_env_obs_dtype()
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test_vecenv()
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test_attr_unwrapped()
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test_async_env()
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test_async_check_id()
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test_env_reset_optional_kwargs()
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test_gym_wrappers()
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