mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
432 lines
16 KiB
Python
432 lines
16 KiB
Python
import gym
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import random
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import unittest
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import ray
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from ray.tune.registry import register_env
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from ray.rllib.agents.pg import PGTrainer
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from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
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from ray.rllib.examples.policy.random_policy import RandomPolicy
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from ray.rllib.examples.env.multi_agent import MultiAgentCartPole, \
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BasicMultiAgent, EarlyDoneMultiAgent, RoundRobinMultiAgent
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from ray.rllib.tests.test_rollout_worker import MockPolicy
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from ray.rllib.evaluation.rollout_worker import RolloutWorker
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from ray.rllib.env.base_env import _MultiAgentEnvToBaseEnv
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def one_hot(i, n):
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out = [0.0] * n
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out[i] = 1.0
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return out
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class TestMultiAgentEnv(unittest.TestCase):
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def setUp(self) -> None:
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ray.init(num_cpus=4)
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def tearDown(self) -> None:
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ray.shutdown()
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def test_basic_mock(self):
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env = BasicMultiAgent(4)
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obs = env.reset()
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self.assertEqual(obs, {0: 0, 1: 0, 2: 0, 3: 0})
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for _ in range(24):
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obs, rew, done, info = env.step({0: 0, 1: 0, 2: 0, 3: 0})
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self.assertEqual(obs, {0: 0, 1: 0, 2: 0, 3: 0})
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self.assertEqual(rew, {0: 1, 1: 1, 2: 1, 3: 1})
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self.assertEqual(done, {
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0: False,
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1: False,
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2: False,
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3: False,
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"__all__": False
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})
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obs, rew, done, info = env.step({0: 0, 1: 0, 2: 0, 3: 0})
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self.assertEqual(done, {
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0: True,
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1: True,
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2: True,
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3: True,
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"__all__": True
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})
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def test_round_robin_mock(self):
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env = RoundRobinMultiAgent(2)
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obs = env.reset()
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self.assertEqual(obs, {0: 0})
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for _ in range(5):
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obs, rew, done, info = env.step({0: 0})
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self.assertEqual(obs, {1: 0})
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self.assertEqual(done["__all__"], False)
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obs, rew, done, info = env.step({1: 0})
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self.assertEqual(obs, {0: 0})
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self.assertEqual(done["__all__"], False)
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obs, rew, done, info = env.step({0: 0})
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self.assertEqual(done["__all__"], True)
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def test_no_reset_until_poll(self):
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env = _MultiAgentEnvToBaseEnv(lambda v: BasicMultiAgent(2), [], 1)
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self.assertFalse(env.get_unwrapped()[0].resetted)
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env.poll()
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self.assertTrue(env.get_unwrapped()[0].resetted)
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def test_vectorize_basic(self):
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env = _MultiAgentEnvToBaseEnv(lambda v: BasicMultiAgent(2), [], 2)
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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self.assertEqual(rew, {0: {0: None, 1: None}, 1: {0: None, 1: None}})
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self.assertEqual(
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dones, {
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0: {
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0: False,
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1: False,
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"__all__": False
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},
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1: {
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0: False,
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1: False,
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"__all__": False
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}
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})
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for _ in range(24):
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env.send_actions({0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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self.assertEqual(rew, {0: {0: 1, 1: 1}, 1: {0: 1, 1: 1}})
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self.assertEqual(
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dones, {
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0: {
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0: False,
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1: False,
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"__all__": False
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},
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1: {
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0: False,
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1: False,
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"__all__": False
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}
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})
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env.send_actions({0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(
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dones, {
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0: {
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0: True,
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1: True,
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"__all__": True
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},
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1: {
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0: True,
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1: True,
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"__all__": True
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}
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})
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# Reset processing
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self.assertRaises(
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ValueError, lambda: env.send_actions({
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0: {
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0: 0,
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1: 0
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},
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1: {
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0: 0,
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1: 0
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}
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}))
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self.assertEqual(env.try_reset(0), {0: 0, 1: 0})
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self.assertEqual(env.try_reset(1), {0: 0, 1: 0})
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env.send_actions({0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}})
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self.assertEqual(rew, {0: {0: 1, 1: 1}, 1: {0: 1, 1: 1}})
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self.assertEqual(
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dones, {
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0: {
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0: False,
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1: False,
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"__all__": False
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},
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1: {
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0: False,
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1: False,
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"__all__": False
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}
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})
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def test_vectorize_round_robin(self):
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env = _MultiAgentEnvToBaseEnv(lambda v: RoundRobinMultiAgent(2), [], 2)
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {0: 0}, 1: {0: 0}})
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self.assertEqual(rew, {0: {0: None}, 1: {0: None}})
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env.send_actions({0: {0: 0}, 1: {0: 0}})
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {1: 0}, 1: {1: 0}})
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env.send_actions({0: {1: 0}, 1: {1: 0}})
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obs, rew, dones, _, _ = env.poll()
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self.assertEqual(obs, {0: {0: 0}, 1: {0: 0}})
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def test_multi_agent_sample(self):
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(2)
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ev = RolloutWorker(
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env_creator=lambda _: BasicMultiAgent(5),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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"p1": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
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rollout_fragment_length=50)
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batch = ev.sample()
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self.assertEqual(batch.count, 50)
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self.assertEqual(batch.policy_batches["p0"].count, 150)
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self.assertEqual(batch.policy_batches["p1"].count, 100)
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self.assertEqual(batch.policy_batches["p0"]["t"].tolist(),
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list(range(25)) * 6)
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def test_multi_agent_sample_sync_remote(self):
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# Allow to be run via Unittest.
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ray.init(num_cpus=4, ignore_reinit_error=True)
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(2)
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ev = RolloutWorker(
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env_creator=lambda _: BasicMultiAgent(5),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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"p1": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
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rollout_fragment_length=50,
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num_envs=4,
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remote_worker_envs=True,
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remote_env_batch_wait_ms=99999999)
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batch = ev.sample()
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self.assertEqual(batch.count, 200)
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def test_multi_agent_sample_async_remote(self):
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# Allow to be run via Unittest.
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ray.init(num_cpus=4, ignore_reinit_error=True)
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(2)
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ev = RolloutWorker(
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env_creator=lambda _: BasicMultiAgent(5),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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"p1": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
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rollout_fragment_length=50,
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num_envs=4,
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remote_worker_envs=True)
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batch = ev.sample()
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self.assertEqual(batch.count, 200)
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def test_multi_agent_sample_with_horizon(self):
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(2)
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ev = RolloutWorker(
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env_creator=lambda _: BasicMultiAgent(5),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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"p1": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
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episode_horizon=10, # test with episode horizon set
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rollout_fragment_length=50)
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batch = ev.sample()
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self.assertEqual(batch.count, 50)
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def test_sample_from_early_done_env(self):
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(2)
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ev = RolloutWorker(
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env_creator=lambda _: EarlyDoneMultiAgent(),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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"p1": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p{}".format(agent_id % 2),
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batch_mode="complete_episodes",
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rollout_fragment_length=1)
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self.assertRaisesRegexp(ValueError,
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".*don't have a last observation.*",
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lambda: ev.sample())
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def test_multi_agent_sample_round_robin(self):
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act_space = gym.spaces.Discrete(2)
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obs_space = gym.spaces.Discrete(10)
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ev = RolloutWorker(
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env_creator=lambda _: RoundRobinMultiAgent(5, increment_obs=True),
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policy={
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"p0": (MockPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p0",
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rollout_fragment_length=50)
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batch = ev.sample()
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self.assertEqual(batch.count, 50)
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# since we round robin introduce agents into the env, some of the env
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# steps don't count as proper transitions
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self.assertEqual(batch.policy_batches["p0"].count, 42)
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self.assertEqual(batch.policy_batches["p0"]["obs"].tolist()[:10], [
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one_hot(0, 10),
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one_hot(1, 10),
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one_hot(2, 10),
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one_hot(3, 10),
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one_hot(4, 10),
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] * 2)
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self.assertEqual(batch.policy_batches["p0"]["new_obs"].tolist()[:10], [
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one_hot(1, 10),
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one_hot(2, 10),
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one_hot(3, 10),
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one_hot(4, 10),
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one_hot(5, 10),
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] * 2)
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self.assertEqual(batch.policy_batches["p0"]["rewards"].tolist()[:10],
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[100, 100, 100, 100, 0] * 2)
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self.assertEqual(batch.policy_batches["p0"]["dones"].tolist()[:10],
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[False, False, False, False, True] * 2)
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self.assertEqual(batch.policy_batches["p0"]["t"].tolist()[:10],
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[4, 9, 14, 19, 24, 5, 10, 15, 20, 25])
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def test_custom_rnn_state_values(self):
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h = {"some": {"arbitrary": "structure", "here": [1, 2, 3]}}
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class StatefulPolicy(RandomPolicy):
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def compute_actions(self,
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obs_batch,
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state_batches=None,
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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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explore=True,
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timestep=None,
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**kwargs):
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return [0] * len(obs_batch), [[h] * len(obs_batch)], {}
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def get_initial_state(self):
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return [{}] # empty dict
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ev = RolloutWorker(
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env_creator=lambda _: gym.make("CartPole-v0"),
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policy=StatefulPolicy,
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rollout_fragment_length=5)
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batch = ev.sample()
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self.assertEqual(batch.count, 5)
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self.assertEqual(batch["state_in_0"][0], {})
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self.assertEqual(batch["state_out_0"][0], h)
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self.assertEqual(batch["state_in_0"][1], h)
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self.assertEqual(batch["state_out_0"][1], h)
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def test_returning_model_based_rollouts_data(self):
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class ModelBasedPolicy(PGTFPolicy):
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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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# Pretend we did a model-based rollout and want to return
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# the extra trajectory.
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builder = episodes[0].new_batch_builder()
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rollout_id = random.randint(0, 10000)
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for t in range(5):
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builder.add_values(
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agent_id="extra_0",
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policy_id="p1", # use p1 so we can easily check it
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t=t,
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eps_id=rollout_id, # new id for each rollout
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obs=obs_batch[0],
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actions=0,
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rewards=0,
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dones=t == 4,
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infos={},
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new_obs=obs_batch[0])
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batch = builder.build_and_reset(episode=None)
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episodes[0].add_extra_batch(batch)
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# Just return zeros for actions
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return [0] * len(obs_batch), [], {}
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single_env = gym.make("CartPole-v0")
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obs_space = single_env.observation_space
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act_space = single_env.action_space
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ev = RolloutWorker(
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env_creator=lambda _: MultiAgentCartPole({"num_agents": 2}),
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policy={
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"p0": (ModelBasedPolicy, obs_space, act_space, {}),
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"p1": (ModelBasedPolicy, obs_space, act_space, {}),
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},
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policy_mapping_fn=lambda agent_id: "p0",
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rollout_fragment_length=5)
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batch = ev.sample()
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self.assertEqual(batch.count, 5)
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self.assertEqual(batch.policy_batches["p0"].count, 10)
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self.assertEqual(batch.policy_batches["p1"].count, 25)
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def test_train_multi_agent_cartpole_single_policy(self):
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n = 10
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register_env("multi_agent_cartpole",
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lambda _: MultiAgentCartPole({"num_agents": n}))
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pg = PGTrainer(
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env="multi_agent_cartpole",
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config={
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"num_workers": 0,
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"framework": "tf",
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})
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for i in range(50):
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result = pg.train()
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print("Iteration {}, reward {}, timesteps {}".format(
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i, result["episode_reward_mean"], result["timesteps_total"]))
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if result["episode_reward_mean"] >= 50 * n:
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return
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raise Exception("failed to improve reward")
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def test_train_multi_agent_cartpole_multi_policy(self):
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n = 10
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register_env("multi_agent_cartpole",
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lambda _: MultiAgentCartPole({"num_agents": n}))
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single_env = gym.make("CartPole-v0")
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def gen_policy():
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config = {
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"gamma": random.choice([0.5, 0.8, 0.9, 0.95, 0.99]),
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"n_step": random.choice([1, 2, 3, 4, 5]),
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}
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obs_space = single_env.observation_space
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act_space = single_env.action_space
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return (None, obs_space, act_space, config)
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pg = PGTrainer(
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env="multi_agent_cartpole",
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config={
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"num_workers": 0,
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"multiagent": {
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"policies": {
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"policy_1": gen_policy(),
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"policy_2": gen_policy(),
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},
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"policy_mapping_fn": lambda agent_id: "policy_1",
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},
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"framework": "tf",
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})
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# Just check that it runs without crashing
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for i in range(10):
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result = pg.train()
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print("Iteration {}, reward {}, timesteps {}".format(
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i, result["episode_reward_mean"], result["timesteps_total"]))
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self.assertTrue(
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pg.compute_action([0, 0, 0, 0], policy_id="policy_1") in [0, 1])
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self.assertTrue(
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pg.compute_action([0, 0, 0, 0], policy_id="policy_2") in [0, 1])
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self.assertRaises(
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KeyError,
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lambda: pg.compute_action([0, 0, 0, 0], policy_id="policy_3"))
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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