mirror of
https://github.com/vale981/ray
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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.
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
import gym
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import numpy as np
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import random
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import unittest
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import ray
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from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
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from ray.rllib.optimizers import SyncSamplesOptimizer
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from ray.rllib.evaluation.rollout_worker import RolloutWorker
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from ray.rllib.evaluation.worker_set import WorkerSet
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from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv
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from ray.rllib.tests.test_rollout_worker import MockPolicy
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from ray.rllib.tests.test_external_env import make_simple_serving
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from ray.rllib.tests.test_multi_agent_env import BasicMultiAgent, MultiCartpole
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from ray.rllib.evaluation.metrics import collect_metrics
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SimpleMultiServing = make_simple_serving(True, ExternalMultiAgentEnv)
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class TestExternalMultiAgentEnv(unittest.TestCase):
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def testExternalMultiAgentEnvCompleteEpisodes(self):
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agents = 4
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ev = RolloutWorker(
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env_creator=lambda _: SimpleMultiServing(BasicMultiAgent(agents)),
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policy=MockPolicy,
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batch_steps=40,
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batch_mode="complete_episodes")
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for _ in range(3):
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batch = ev.sample()
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self.assertEqual(batch.count, 40)
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self.assertEqual(len(np.unique(batch["agent_index"])), agents)
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def testExternalMultiAgentEnvTruncateEpisodes(self):
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agents = 4
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ev = RolloutWorker(
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env_creator=lambda _: SimpleMultiServing(BasicMultiAgent(agents)),
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policy=MockPolicy,
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batch_steps=40,
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batch_mode="truncate_episodes")
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for _ in range(3):
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batch = ev.sample()
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self.assertEqual(batch.count, 160)
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self.assertEqual(len(np.unique(batch["agent_index"])), agents)
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def testExternalMultiAgentEnvSample(self):
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agents = 2
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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 _: SimpleMultiServing(BasicMultiAgent(agents)),
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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_steps=50)
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batch = ev.sample()
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self.assertEqual(batch.count, 50)
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def testTrainExternalMultiCartpoleManyPolicies(self):
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n = 20
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single_env = gym.make("CartPole-v0")
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act_space = single_env.action_space
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obs_space = single_env.observation_space
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policies = {}
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for i in range(20):
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policies["pg_{}".format(i)] = (PGTFPolicy, obs_space, act_space,
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{})
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policy_ids = list(policies.keys())
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ev = RolloutWorker(
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env_creator=lambda _: MultiCartpole(n),
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policy=policies,
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policy_mapping_fn=lambda agent_id: random.choice(policy_ids),
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batch_steps=100)
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optimizer = SyncSamplesOptimizer(WorkerSet._from_existing(ev))
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for i in range(100):
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optimizer.step()
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result = collect_metrics(ev)
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print("Iteration {}, rew {}".format(i,
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result["policy_reward_mean"]))
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print("Total reward", result["episode_reward_mean"])
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if result["episode_reward_mean"] >= 25 * n:
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return
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raise Exception("failed to improve reward")
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if __name__ == "__main__":
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ray.init()
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unittest.main(verbosity=2)
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