mirror of
https://github.com/wassname/ray.git
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Eric Liang
aa014af85b
Changes needed to reproduce Atari plots in IMPALA / A2C: https://github.com/ray-project/rl-experiments
353 lines
12 KiB
Python
353 lines
12 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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import time
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import unittest
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import ray
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from ray.rllib.agents.pg import PGAgent
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from ray.rllib.agents.a3c import A2CAgent
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from ray.rllib.evaluation.policy_evaluator import PolicyEvaluator
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from ray.rllib.evaluation.metrics import collect_metrics
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from ray.rllib.evaluation.policy_graph import PolicyGraph
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from ray.rllib.evaluation.postprocessing import compute_advantages
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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 MockPolicyGraph(PolicyGraph):
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def compute_actions(self,
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obs_batch,
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state_batches,
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is_training=False,
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episodes=None):
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return [0] * len(obs_batch), [], {}
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def postprocess_trajectory(self, batch, other_agent_batches=None):
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return compute_advantages(batch, 100.0, 0.9, use_gae=False)
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class BadPolicyGraph(PolicyGraph):
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def compute_actions(self,
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obs_batch,
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state_batches,
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is_training=False,
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episodes=None):
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raise Exception("intentional error")
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def postprocess_trajectory(self, batch, other_agent_batches=None):
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return compute_advantages(batch, 100.0, 0.9, use_gae=False)
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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 TestPolicyEvaluator(unittest.TestCase):
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def testBasic(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy_graph=MockPolicyGraph)
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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 testGlobalVarsUpdate(self):
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agent = A2CAgent(
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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 testQueryEvaluators(self):
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register_env("test", lambda _: gym.make("CartPole-v0"))
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pg = PGAgent(
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env="test", config={
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"num_workers": 2,
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"sample_batch_size": 5
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})
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results = pg.optimizer.foreach_evaluator(lambda ev: ev.batch_steps)
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results2 = pg.optimizer.foreach_evaluator_with_index(
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lambda ev, i: (i, ev.batch_steps))
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self.assertEqual(results, [5, 5, 5])
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self.assertEqual(results2, [(0, 5), (1, 5), (2, 5)])
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def testRewardClipping(self):
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# clipping on
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ev = PolicyEvaluator(
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env_creator=lambda _: MockEnv2(episode_length=10),
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policy_graph=MockPolicyGraph,
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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 = PolicyEvaluator(
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env_creator=lambda _: MockEnv2(episode_length=10),
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policy_graph=MockPolicyGraph,
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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 testMetrics(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy_graph=MockPolicyGraph,
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batch_mode="complete_episodes")
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remote_ev = PolicyEvaluator.as_remote().remote(
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env_creator=lambda _: MockEnv(episode_length=10),
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policy_graph=MockPolicyGraph,
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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_total"], 20)
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self.assertEqual(result["episode_reward_mean"], 10)
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def testAsync(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: gym.make("CartPole-v0"),
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sample_async=True,
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policy_graph=MockPolicyGraph)
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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 testAutoConcat(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: MockEnv(episode_length=40),
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policy_graph=MockPolicyGraph,
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sample_async=True,
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batch_steps=10,
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batch_mode="truncate_episodes",
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observation_filter="ConcurrentMeanStdFilter")
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time.sleep(2)
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batch = ev.sample()
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self.assertEqual(batch.count, 40) # auto-concat up to 5 episodes
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def testAutoVectorization(self):
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ev = PolicyEvaluator(
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env_creator=lambda cfg: MockEnv(episode_length=20, config=cfg),
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policy_graph=MockPolicyGraph,
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batch_mode="truncate_episodes",
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batch_steps=16,
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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_total"], 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_total"], 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 testBatchDivisibilityCheck(self):
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self.assertRaises(
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ValueError,
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lambda: PolicyEvaluator(
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env_creator=lambda _: MockEnv(episode_length=8),
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policy_graph=MockPolicyGraph,
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batch_mode="truncate_episodes",
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batch_steps=15, num_envs=4))
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def testBatchesSmallerWhenVectorized(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: MockEnv(episode_length=8),
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policy_graph=MockPolicyGraph,
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batch_mode="truncate_episodes",
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batch_steps=16,
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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_total"], 0)
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batch = ev.sample()
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result = collect_metrics(ev, [])
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self.assertEqual(result["episodes_total"], 4)
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def testVectorEnvSupport(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: MockVectorEnv(episode_length=20, num_envs=8),
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policy_graph=MockPolicyGraph,
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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_total"], 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_total"], 8)
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def testTruncateEpisodes(self):
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ev = PolicyEvaluator(
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env_creator=lambda _: MockEnv(10),
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policy_graph=MockPolicyGraph,
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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 = PolicyEvaluator(
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env_creator=lambda _: MockEnv(10),
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policy_graph=MockPolicyGraph,
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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 = PolicyEvaluator(
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env_creator=lambda _: MockEnv(10),
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policy_graph=MockPolicyGraph,
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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 = PolicyEvaluator(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy_graph=MockPolicyGraph,
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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"]
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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 = PolicyEvaluator(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy_graph=MockPolicyGraph,
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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"]
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obs_f2 = filters2["default"]
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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 = PolicyEvaluator(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy_graph=MockPolicyGraph,
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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"]
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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": new_obsf})
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filters = ev.get_filters(flush_after=False)
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obs_f = filters["default"]
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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"]
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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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