mirror of
https://github.com/wassname/ray.git
synced 2026-07-18 12:40:56 +08:00
SvenandEric Liang
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
443 lines
15 KiB
Python
443 lines
15 KiB
Python
import gym
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import numpy as np
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import random
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import time
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import unittest
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from collections import Counter
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import ray
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from ray.rllib.agents.pg import PGTrainer
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from ray.rllib.agents.a3c import A2CTrainer
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from ray.rllib.evaluation.rollout_worker import RolloutWorker
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from ray.rllib.evaluation.metrics import collect_metrics
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from ray.rllib.policy.policy import Policy
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from ray.rllib.evaluation.postprocessing import compute_advantages
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from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch
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from ray.rllib.env.vector_env import VectorEnv
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from ray.tune.registry import register_env
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class MockPolicy(Policy):
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def compute_actions(self,
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obs_batch,
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state_batches,
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prev_action_batch=None,
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prev_reward_batch=None,
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episodes=None,
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**kwargs):
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return [random.choice([0, 1])] * len(obs_batch), [], {}
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def postprocess_trajectory(self,
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batch,
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other_agent_batches=None,
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episode=None):
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assert episode is not None
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return compute_advantages(batch, 100.0, 0.9, use_gae=False)
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class BadPolicy(Policy):
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def compute_actions(self,
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obs_batch,
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state_batches,
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prev_action_batch=None,
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prev_reward_batch=None,
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episodes=None,
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**kwargs):
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raise Exception("intentional error")
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def postprocess_trajectory(self,
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batch,
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other_agent_batches=None,
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episode=None):
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assert episode is not None
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return compute_advantages(batch, 100.0, 0.9, use_gae=False)
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class FailOnStepEnv(gym.Env):
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def __init__(self):
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self.observation_space = gym.spaces.Discrete(1)
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self.action_space = gym.spaces.Discrete(2)
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def reset(self):
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raise ValueError("kaboom")
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def step(self, action):
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raise ValueError("kaboom")
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class MockEnv(gym.Env):
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def __init__(self, episode_length, config=None):
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self.episode_length = episode_length
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self.config = config
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self.i = 0
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self.observation_space = gym.spaces.Discrete(1)
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self.action_space = gym.spaces.Discrete(2)
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def reset(self):
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self.i = 0
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return self.i
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def step(self, action):
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self.i += 1
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return 0, 1, self.i >= self.episode_length, {}
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class MockEnv2(gym.Env):
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def __init__(self, episode_length):
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self.episode_length = episode_length
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self.i = 0
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self.observation_space = gym.spaces.Discrete(100)
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self.action_space = gym.spaces.Discrete(2)
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def reset(self):
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self.i = 0
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return self.i
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def step(self, action):
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self.i += 1
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return self.i, 100, self.i >= self.episode_length, {}
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class MockVectorEnv(VectorEnv):
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def __init__(self, episode_length, num_envs):
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self.envs = [MockEnv(episode_length) for _ in range(num_envs)]
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self.observation_space = gym.spaces.Discrete(1)
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self.action_space = gym.spaces.Discrete(2)
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self.num_envs = num_envs
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def vector_reset(self):
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return [e.reset() for e in self.envs]
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def reset_at(self, index):
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return self.envs[index].reset()
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def vector_step(self, actions):
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obs_batch, rew_batch, done_batch, info_batch = [], [], [], []
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for i in range(len(self.envs)):
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obs, rew, done, info = self.envs[i].step(actions[i])
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obs_batch.append(obs)
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rew_batch.append(rew)
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done_batch.append(done)
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info_batch.append(info)
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return obs_batch, rew_batch, done_batch, info_batch
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def get_unwrapped(self):
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return self.envs
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class TestRolloutWorker(unittest.TestCase):
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def testBasic(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"), policy=MockPolicy)
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batch = ev.sample()
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for key in [
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"obs", "actions", "rewards", "dones", "advantages",
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"prev_rewards", "prev_actions"
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]:
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self.assertIn(key, batch)
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self.assertGreater(np.abs(np.mean(batch[key])), 0)
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def to_prev(vec):
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out = np.zeros_like(vec)
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for i, v in enumerate(vec):
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if i + 1 < len(out) and not batch["dones"][i]:
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out[i + 1] = v
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return out.tolist()
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self.assertEqual(batch["prev_rewards"].tolist(),
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to_prev(batch["rewards"]))
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self.assertEqual(batch["prev_actions"].tolist(),
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to_prev(batch["actions"]))
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self.assertGreater(batch["advantages"][0], 1)
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def testBatchIds(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"), policy=MockPolicy)
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batch1 = ev.sample()
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batch2 = ev.sample()
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self.assertEqual(len(set(batch1["unroll_id"])), 1)
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self.assertEqual(len(set(batch2["unroll_id"])), 1)
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self.assertEqual(
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len(set(SampleBatch.concat(batch1, batch2)["unroll_id"])), 2)
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def testGlobalVarsUpdate(self):
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agent = A2CTrainer(
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env="CartPole-v0",
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config={
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"lr_schedule": [[0, 0.1], [400, 0.000001]],
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})
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result = agent.train()
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self.assertGreater(result["info"]["learner"]["cur_lr"], 0.01)
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result2 = agent.train()
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self.assertLess(result2["info"]["learner"]["cur_lr"], 0.0001)
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def testNoStepOnInit(self):
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register_env("fail", lambda _: FailOnStepEnv())
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pg = PGTrainer(env="fail", config={"num_workers": 1})
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self.assertRaises(Exception, lambda: pg.train())
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def testCallbacks(self):
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counts = Counter()
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pg = PGTrainer(
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env="CartPole-v0", config={
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"num_workers": 0,
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"sample_batch_size": 50,
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"train_batch_size": 50,
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"callbacks": {
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"on_episode_start": lambda x: counts.update({"start": 1}),
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"on_episode_step": lambda x: counts.update({"step": 1}),
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"on_episode_end": lambda x: counts.update({"end": 1}),
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"on_sample_end": lambda x: counts.update({"sample": 1}),
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},
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})
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pg.train()
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pg.train()
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pg.train()
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pg.train()
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self.assertEqual(counts["sample"], 4)
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self.assertGreater(counts["start"], 0)
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self.assertGreater(counts["end"], 0)
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self.assertGreater(counts["step"], 200)
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self.assertLess(counts["step"], 400)
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def testQueryEvaluators(self):
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register_env("test", lambda _: gym.make("CartPole-v0"))
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pg = PGTrainer(
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env="test",
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config={
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"num_workers": 2,
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"sample_batch_size": 5,
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"num_envs_per_worker": 2,
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})
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results = pg.workers.foreach_worker(lambda ev: ev.sample_batch_size)
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results2 = pg.workers.foreach_worker_with_index(
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lambda ev, i: (i, ev.sample_batch_size))
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results3 = pg.workers.foreach_worker(
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lambda ev: ev.foreach_env(lambda env: 1))
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self.assertEqual(results, [10, 10, 10])
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self.assertEqual(results2, [(0, 10), (1, 10), (2, 10)])
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self.assertEqual(results3, [[1, 1], [1, 1], [1, 1]])
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def testRewardClipping(self):
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# clipping on
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv2(episode_length=10),
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policy=MockPolicy,
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clip_rewards=True,
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batch_mode="complete_episodes")
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self.assertEqual(max(ev.sample()["rewards"]), 1)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episode_reward_mean"], 1000)
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# clipping off
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ev2 = RolloutWorker(
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env_creator=lambda _: MockEnv2(episode_length=10),
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policy=MockPolicy,
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clip_rewards=False,
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batch_mode="complete_episodes")
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self.assertEqual(max(ev2.sample()["rewards"]), 100)
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result2 = collect_metrics(ev2, [])
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self.assertEqual(result2["episode_reward_mean"], 1000)
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def testHardHorizon(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy=MockPolicy,
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batch_mode="complete_episodes",
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batch_steps=10,
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episode_horizon=4,
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soft_horizon=False)
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samples = ev.sample()
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# three logical episodes
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self.assertEqual(len(set(samples["eps_id"])), 3)
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# 3 done values
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self.assertEqual(sum(samples["dones"]), 3)
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def testSoftHorizon(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy=MockPolicy,
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batch_mode="complete_episodes",
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batch_steps=10,
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episode_horizon=4,
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soft_horizon=True)
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samples = ev.sample()
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# three logical episodes
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self.assertEqual(len(set(samples["eps_id"])), 3)
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# only 1 hard done value
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self.assertEqual(sum(samples["dones"]), 1)
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def testMetrics(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy=MockPolicy,
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batch_mode="complete_episodes")
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remote_ev = RolloutWorker.as_remote().remote(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy=MockPolicy,
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batch_mode="complete_episodes")
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ev.sample()
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ray.get(remote_ev.sample.remote())
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result = collect_metrics(ev, [remote_ev])
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self.assertEqual(result["episodes_this_iter"], 20)
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self.assertEqual(result["episode_reward_mean"], 10)
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def testAsync(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"),
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sample_async=True,
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policy=MockPolicy)
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batch = ev.sample()
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for key in ["obs", "actions", "rewards", "dones", "advantages"]:
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self.assertIn(key, batch)
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self.assertGreater(batch["advantages"][0], 1)
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def testAutoVectorization(self):
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ev = RolloutWorker(
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env_creator=lambda cfg: MockEnv(episode_length=20, config=cfg),
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policy=MockPolicy,
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batch_mode="truncate_episodes",
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batch_steps=2,
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num_envs=8)
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for _ in range(8):
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batch = ev.sample()
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self.assertEqual(batch.count, 16)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 0)
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for _ in range(8):
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batch = ev.sample()
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self.assertEqual(batch.count, 16)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 8)
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indices = []
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for env in ev.async_env.vector_env.envs:
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self.assertEqual(env.unwrapped.config.worker_index, 0)
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indices.append(env.unwrapped.config.vector_index)
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self.assertEqual(indices, [0, 1, 2, 3, 4, 5, 6, 7])
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def testBatchesLargerWhenVectorized(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(episode_length=8),
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policy=MockPolicy,
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batch_mode="truncate_episodes",
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batch_steps=4,
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num_envs=4)
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batch = ev.sample()
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self.assertEqual(batch.count, 16)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 0)
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batch = ev.sample()
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 4)
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def testVectorEnvSupport(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockVectorEnv(episode_length=20, num_envs=8),
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policy=MockPolicy,
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batch_mode="truncate_episodes",
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batch_steps=10)
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for _ in range(8):
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batch = ev.sample()
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self.assertEqual(batch.count, 10)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 0)
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for _ in range(8):
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batch = ev.sample()
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self.assertEqual(batch.count, 10)
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_this_iter"], 8)
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def testTruncateEpisodes(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(10),
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policy=MockPolicy,
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batch_steps=15,
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batch_mode="truncate_episodes")
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batch = ev.sample()
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self.assertEqual(batch.count, 15)
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def testCompleteEpisodes(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(10),
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policy=MockPolicy,
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batch_steps=5,
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batch_mode="complete_episodes")
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batch = ev.sample()
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self.assertEqual(batch.count, 10)
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def testCompleteEpisodesPacking(self):
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ev = RolloutWorker(
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env_creator=lambda _: MockEnv(10),
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policy=MockPolicy,
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batch_steps=15,
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batch_mode="complete_episodes")
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batch = ev.sample()
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self.assertEqual(batch.count, 20)
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self.assertEqual(
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batch["t"].tolist(),
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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def testFilterSync(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy=MockPolicy,
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sample_async=True,
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observation_filter="ConcurrentMeanStdFilter")
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time.sleep(2)
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ev.sample()
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filters = ev.get_filters(flush_after=True)
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obs_f = filters[DEFAULT_POLICY_ID]
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self.assertNotEqual(obs_f.rs.n, 0)
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self.assertNotEqual(obs_f.buffer.n, 0)
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def testGetFilters(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy=MockPolicy,
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sample_async=True,
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observation_filter="ConcurrentMeanStdFilter")
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self.sample_and_flush(ev)
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filters = ev.get_filters(flush_after=False)
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time.sleep(2)
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filters2 = ev.get_filters(flush_after=False)
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obs_f = filters[DEFAULT_POLICY_ID]
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obs_f2 = filters2[DEFAULT_POLICY_ID]
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self.assertGreaterEqual(obs_f2.rs.n, obs_f.rs.n)
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self.assertGreaterEqual(obs_f2.buffer.n, obs_f.buffer.n)
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def testSyncFilter(self):
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy=MockPolicy,
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sample_async=True,
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observation_filter="ConcurrentMeanStdFilter")
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obs_f = self.sample_and_flush(ev)
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# Current State
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filters = ev.get_filters(flush_after=False)
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obs_f = filters[DEFAULT_POLICY_ID]
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self.assertLessEqual(obs_f.buffer.n, 20)
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new_obsf = obs_f.copy()
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new_obsf.rs._n = 100
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ev.sync_filters({DEFAULT_POLICY_ID: new_obsf})
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filters = ev.get_filters(flush_after=False)
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obs_f = filters[DEFAULT_POLICY_ID]
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self.assertGreaterEqual(obs_f.rs.n, 100)
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self.assertLessEqual(obs_f.buffer.n, 20)
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def sample_and_flush(self, ev):
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time.sleep(2)
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ev.sample()
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filters = ev.get_filters(flush_after=True)
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obs_f = filters[DEFAULT_POLICY_ID]
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self.assertNotEqual(obs_f.rs.n, 0)
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self.assertNotEqual(obs_f.buffer.n, 0)
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return obs_f
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if __name__ == "__main__":
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ray.init(num_cpus=5)
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unittest.main(verbosity=2)
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