def testServingEnvHorizonNotSupported(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: SimpleServing(MockEnv(25)),
policy_graph=MockPolicyGraph,
episode_horizon=20,
batch_steps=10,
batch_mode="complete_episodes")
ev.sample()
self.assertRaises(Exception, lambda: ev.sample())
def testFilterSync(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: gym.make("CartPole-v0"),
policy_graph=MockPolicyGraph,
sample_async=True,
observation_filter="ConcurrentMeanStdFilter")
time.sleep(2)
ev.sample()
filters = ev.get_filters(flush_after=True)
obs_f = filters["default"]
self.assertNotEqual(obs_f.rs.n, 0)
self.assertNotEqual(obs_f.buffer.n, 0)
def testMetrics(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(episode_length=10),
policy_graph=MockPolicyGraph, batch_mode="complete_episodes")
remote_ev = CommonPolicyEvaluator.as_remote().remote(
env_creator=lambda _: MockEnv(episode_length=10),
policy_graph=MockPolicyGraph, batch_mode="complete_episodes")
ev.sample()
ray.get(remote_ev.sample.remote())
result = collect_metrics(ev, [remote_ev])
self.assertEqual(result.episodes_total, 20)
self.assertEqual(result.episode_reward_mean, 10)
def testBatchesSmallerWhenVectorized(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(episode_length=8),
policy_graph=MockPolicyGraph,
batch_mode="truncate_episodes",
batch_steps=16, num_envs=4)
batch = ev.sample()
self.assertEqual(batch.count, 16)
result = collect_metrics(ev, [])
self.assertEqual(result.episodes_total, 0)
batch = ev.sample()
result = collect_metrics(ev, [])
self.assertEqual(result.episodes_total, 4)
def testAutoVectorization(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(episode_length=20),
policy_graph=MockPolicyGraph,
batch_mode="truncate_episodes",
batch_steps=16, num_envs=8)
for _ in range(8):
batch = ev.sample()
self.assertEqual(batch.count, 16)
result = collect_metrics(ev, [])
self.assertEqual(result.episodes_total, 0)
for _ in range(8):
batch = ev.sample()
self.assertEqual(batch.count, 16)
result = collect_metrics(ev, [])
self.assertEqual(result.episodes_total, 8)
def testMultiAgentSampleRoundRobin(self):
act_space = gym.spaces.Discrete(2)
obs_space = gym.spaces.Discrete(2)
ev = CommonPolicyEvaluator(
env_creator=lambda _: RoundRobinMultiAgent(5, increment_obs=True),
policy_graph={
"p0": (MockPolicyGraph, obs_space, act_space, {}),
},
policy_mapping_fn=lambda agent_id: "p0",
batch_steps=50)
batch = ev.sample()
self.assertEqual(batch.count, 50)
# since we round robin introduce agents into the env, some of the env
# steps don't count as proper transitions
self.assertEqual(batch.policy_batches["p0"].count, 42)
self.assertEqual(
batch.policy_batches["p0"]["obs"].tolist()[:10],
[0, 1, 2, 3, 4] * 2)
self.assertEqual(
batch.policy_batches["p0"]["new_obs"].tolist()[:10],
[1, 2, 3, 4, 5] * 2)
self.assertEqual(
batch.policy_batches["p0"]["rewards"].tolist()[:10],
[100, 100, 100, 100, 0] * 2)
self.assertEqual(
batch.policy_batches["p0"]["dones"].tolist()[:10],
[False, False, False, False, True] * 2)
self.assertEqual(
batch.policy_batches["p0"]["t"].tolist()[:10],
[4, 9, 14, 19, 24, 5, 10, 15, 20, 25])
def testBasic(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: gym.make("CartPole-v0"),
policy_graph=MockPolicyGraph)
batch = ev.sample()
for key in ["obs", "actions", "rewards", "dones", "advantages"]:
self.assertIn(key, batch)
self.assertGreater(batch["advantages"][0], 1)
def testCompleteEpisodes(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(10),
policy_graph=MockPolicyGraph,
batch_steps=5,
batch_mode="complete_episodes")
batch = ev.sample()
self.assertEqual(batch.count, 10)
def testServingEnvBadActions(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: SimpleServing(MockEnv(25)),
policy_graph=BadPolicyGraph,
sample_async=True,
batch_steps=40,
batch_mode="truncate_episodes")
self.assertRaises(Exception, lambda: ev.sample())
def testServingEnvTruncateEpisodes(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: SimpleServing(MockEnv(25)),
policy_graph=MockPolicyGraph,
batch_steps=40,
batch_mode="truncate_episodes")
for _ in range(3):
batch = ev.sample()
self.assertEqual(batch.count, 40)
def testCompleteEpisodesPacking(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(10),
policy_graph=MockPolicyGraph,
batch_steps=15,
batch_mode="complete_episodes")
batch = ev.sample()
self.assertEqual(batch.count, 20)
self.assertEqual(
batch["t"].tolist(),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
def testAutoConcat(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: MockEnv(episode_length=40),
policy_graph=MockPolicyGraph,
sample_async=True,
batch_steps=10,
batch_mode="truncate_episodes",
observation_filter="ConcurrentMeanStdFilter")
time.sleep(2)
batch = ev.sample()
self.assertEqual(batch.count, 40) # auto-concat up to 5 episodes
def testServingEnvOffPolicy(self):
ev = CommonPolicyEvaluator(
env_creator=lambda _: SimpleOffPolicyServing(MockEnv(25), 42),
policy_graph=MockPolicyGraph,
batch_steps=40,
batch_mode="complete_episodes")
for _ in range(3):
batch = ev.sample()
self.assertEqual(batch.count, 50)
self.assertEqual(batch["actions"][0], 42)
self.assertEqual(batch["actions"][-1], 42)
def testMultiAgentSample(self):
act_space = gym.spaces.Discrete(2)
obs_space = gym.spaces.Discrete(2)
ev = CommonPolicyEvaluator(
env_creator=lambda _: BasicMultiAgent(5),
policy_graph={
"p0": (MockPolicyGraph, obs_space, act_space, {}),
"p1": (MockPolicyGraph, obs_space, act_space, {}),
},
policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
batch_steps=50)
batch = ev.sample()
self.assertEqual(batch.count, 50)
self.assertEqual(batch.policy_batches["p0"].count, 150)
self.assertEqual(batch.policy_batches["p1"].count, 100)
self.assertEqual(
batch.policy_batches["p0"]["t"].tolist(),
list(range(25)) * 6)