def test_integration_with_cartpole():
env = envs.CartPole()
agent = agents.DeterministicMCTSAgent(
action_space=env.action_space,
n_passes=2,
)
episode = testing.run_with_dummy_network(agent.solve(env))
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_integration_with_cartpole():
env = envs.CartPole()
agent = agents.DeterministicMCTSAgent(n_passes=2)
network_sig = agent.network_signature(env.observation_space,
env.action_space)
episode = testing.run_with_dummy_network_prediction(
agent.solve(env), network_sig)
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_integration_with_cartpole():
# Set up
env = envs.CartPole()
agent = agents.ShootingAgent(n_rollouts=1)
# Run
episode = testing.run_with_dummy_network_response(agent.solve(env))
# Test
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_integration_with_cartpole():
# Set up
env = envs.CartPole()
agent = agents.ShootingAgent(action_space=env.action_space, n_rollouts=1)
# Run
episode = testing.run_without_suspensions(agent.solve(env))
# Test
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_integration_with_cartpole():
env = envs.CartPole()
agent = agents.StochasticMCTSAgent(
n_passes=2,
new_leaf_rater_class=functools.partial(
agents.stochastic_mcts.RolloutNewLeafRater,
rollout_time_limit=2,
),
)
episode = testing.run_without_suspensions(agent.solve(env))
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_integration_with_cartpole(graph_mode):
env = envs.CartPole()
agent = agents.StochasticMCTSAgent(
action_space=env.action_space,
n_passes=2,
rate_new_leaves_fn=functools.partial(
agents.stochastic_mcts.rate_new_leaves_with_rollouts,
rollout_time_limit=2,
),
graph_mode=graph_mode,
)
episode = testing.run_without_suspensions(agent.solve(env))
assert episode.transition_batch.observation.shape[0] # pylint: disable=no-member
def test_act_doesnt_change_env_state():
# Set up
env = envs.CartPole()
agent = agents.ShootingAgent(n_rollouts=10)
observation = env.reset()
testing.run_with_dummy_network_response(agent.reset(env, observation))
# Run
state_before = env.clone_state()
testing.run_without_suspensions(agent.act(observation))
state_after = env.clone_state()
# Test
np.testing.assert_equal(state_before, state_after)
def test_batch_steppers_different_requests_single_batch(batch_stepper_cls):
xparams = 'params'
n_envs = 9
n_requests = 100
agent_network_fn = functools.partial(
_TestPredictNetwork, request_type=RequestType.AGENT_PREDICTION)
model_network_fn = functools.partial(
_TestPredictNetwork, request_type=RequestType.MODEL_PREDICTION)
bs = batch_stepper_cls(
env_class=envs.CartPole,
agent_class=RandomAgent, # This is only placeholder.
network_fn=agent_network_fn, # This is only placeholder.
model_network_fn=model_network_fn, # This is only placeholder.
n_envs=n_envs,
output_dir=None,
)
envs_and_agents = [
(envs.CartPole(),
_TestAgentYieldingNetworkRequest(agent_network_fn, xparams,
n_requests))
for _ in range(n_envs // 3)
]
envs_and_agents += [(envs.CartPole(),
_TestAgentYieldingAgentPredictRequest(n_requests))
for _ in range(n_envs // 3)]
envs_and_agents += [(envs.CartPole(),
_TestAgentYieldingModelPredictRequest(n_requests))
for _ in range(n_envs // 3)]
# Envs and agents have to be replaced here manually, because
# LocalBatchStepper does not support different types of agents to be run
# simultaneously.
bs._envs_and_agents = envs_and_agents # pylint: disable=protected-access
bs.run_episode_batch(xparams)
def test_act_doesnt_change_env_state(graph_mode, rate_new_leaves_fn):
env = envs.CartPole()
agent = agents.StochasticMCTSAgent(
action_space=env.action_space,
n_passes=2,
rate_new_leaves_fn=rate_new_leaves_fn,
graph_mode=graph_mode,
)
observation = env.reset()
testing.run_without_suspensions(agent.reset(env, observation))
state_before = env.clone_state()
testing.run_with_dummy_network(agent.act(observation))
state_after = env.clone_state()
np.testing.assert_equal(state_before, state_after)
def test_act_doesnt_change_env_state(new_leaf_rater_class):
env = envs.CartPole()
agent = agents.StochasticMCTSAgent(
n_passes=2,
new_leaf_rater_class=new_leaf_rater_class,
)
observation = env.reset()
testing.run_without_suspensions(agent.reset(env, observation))
state_before = env.clone_state()
network_sig = agent.network_signature(env.observation_space,
env.action_space)
testing.run_with_dummy_network_prediction(agent.act(observation),
network_sig)
state_after = env.clone_state()
np.testing.assert_equal(state_before, state_after)
