def testTrainCartpole(self):
register_env("test", lambda _: SimpleServing(gym.make("CartPole-v0")))
pg = PGAgent(env="test", config={"num_workers": 0})
for i in range(100):
result = pg.train()
print("Iteration {}, reward {}, timesteps {}".format(
i, result.episode_reward_mean, result.timesteps_total))
if result.episode_reward_mean >= 100:
return
raise Exception("failed to improve reward")
def testTrainMultiCartpoleSinglePolicy(self):
n = 10
register_env("multi_cartpole", lambda _: MultiCartpole(n))
pg = PGAgent(env="multi_cartpole", config={"num_workers": 0})
for i in range(100):
result = pg.train()
print("Iteration {}, reward {}, timesteps {}".format(
i, result.episode_reward_mean, result.timesteps_total))
if result.episode_reward_mean >= 50 * n:
return
raise Exception("failed to improve reward")
def testQueryEvaluators(self):
register_env("test", lambda _: gym.make("CartPole-v0"))
pg = PGAgent(env="test", config={"num_workers": 2, "batch_size": 5})
results = pg.optimizer.foreach_evaluator(lambda ev: ev.batch_steps)
results2 = pg.optimizer.foreach_evaluator_with_index(
lambda ev, i: (i, ev.batch_steps))
self.assertEqual(results, [5, 5, 5])
self.assertEqual(results2, [(0, 5), (1, 5), (2, 5)])