def test_examples_part2():
"""
Tests snippets in maze/docs/source/concepts_and_structure/run_context_overview.rst.
Adds some performance-specific configuration that should not influence snippets' functionality.
Split for runtime reasons.
"""
a2c_overrides = {"runner.concurrency": 1}
es_overrides = {
"algorithm.n_epochs": 1,
"algorithm.n_rollouts_per_update": 1
}
env_factory = lambda: GymMazeEnv('CartPole-v0')
alg_config = _get_alg_config('CartPole-v0', "dev")
# ------------------------------------------------------------------
rc = RunContext(algorithm=alg_config,
runner="dev",
overrides=a2c_overrides,
configuration="test")
rc.train(n_epochs=1)
# ------------------------------------------------------------------
with pytest.raises(omegaconf.errors.ConfigAttributeError):
rc = RunContext(overrides={"algorithm": alg_config}, runner="dev")
rc.train(n_epochs=1)
# ------------------------------------------------------------------
rc = RunContext(env=lambda: env_factory(),
overrides=es_overrides,
runner="dev",
configuration="test")
rc.train(n_epochs=1)
# Run trained policy.
env = env_factory()
obs = env.reset()
for i in range(10):
action = rc.compute_action(obs)
obs, rewards, dones, info = env.step(action)
# ------------------------------------------------------------------
rc = RunContext(env=lambda: GymMazeEnv('CartPole-v0'),
overrides=es_overrides,
runner="dev",
configuration="test")
rc.train()
rc.evaluate()
def test_readme():
"""
Tests snippets in readme.md.
"""
rc = RunContext(env=lambda: GymMazeEnv('CartPole-v0'))
rc.train(n_epochs=1)
# Run trained policy.
env = GymMazeEnv('CartPole-v0')
obs = env.reset()
done = False
while not done:
action = rc.compute_action(obs)
obs, reward, done, info = env.step(action)
break
def test_concepts_and_structures_run_context_overview():
"""
Tests snippets in docs/source/concepts_and_structure/run_context_overview.rst.
"""
# Default overrides for faster tests. Shouldn't change functionality.
ac_overrides = {"runner.concurrency": 1}
es_overrides = {"algorithm.n_epochs": 1, "algorithm.n_rollouts_per_update": 1}
# Training
# --------
rc = RunContext(
algorithm="a2c",
overrides={"env.name": "CartPole-v0", **ac_overrides},
model="vector_obs",
critic="template_state",
runner="dev",
configuration="test"
)
rc.train(n_epochs=1)
alg_config = A2CAlgorithmConfig(
n_epochs=1,
epoch_length=25,
patience=15,
critic_burn_in_epochs=0,
n_rollout_steps=100,
lr=0.0005,
gamma=0.98,
gae_lambda=1.0,
policy_loss_coef=1.0,
value_loss_coef=0.5,
entropy_coef=0.00025,
max_grad_norm=0.0,
device='cpu',
rollout_evaluator=RolloutEvaluator(
eval_env=SequentialVectorEnv([lambda: GymMazeEnv("CartPole-v0")]),
n_episodes=1,
model_selection=None,
deterministic=True
)
)
rc = RunContext(
algorithm=alg_config,
overrides={"env.name": "CartPole-v0", **ac_overrides},
model="vector_obs",
critic="template_state",
runner="dev",
configuration="test"
)
rc.train(n_epochs=1)
rc = RunContext(env=lambda: GymMazeEnv('CartPole-v0'), overrides=es_overrides, runner="dev", configuration="test")
rc.train(n_epochs=1)
policy_composer_config = {
'_target_': 'maze.perception.models.policies.ProbabilisticPolicyComposer',
'networks': [{
'_target_': 'maze.perception.models.built_in.flatten_concat.FlattenConcatPolicyNet',
'non_lin': 'torch.nn.Tanh',
'hidden_units': [256, 256]
}],
"substeps_with_separate_agent_nets": [],
"agent_counts_dict": {0: 1}
}
rc = RunContext(
overrides={"model.policy": policy_composer_config, **es_overrides}, runner="dev", configuration="test"
)
rc.train(n_epochs=1)
env = GymMazeEnv('CartPole-v0')
policy_composer = ProbabilisticPolicyComposer(
action_spaces_dict=env.action_spaces_dict,
observation_spaces_dict=env.observation_spaces_dict,
distribution_mapper=DistributionMapper(action_space=env.action_space, distribution_mapper_config={}),
networks=[{
'_target_': 'maze.perception.models.built_in.flatten_concat.FlattenConcatPolicyNet',
'non_lin': 'torch.nn.Tanh',
'hidden_units': [222, 222]
}],
substeps_with_separate_agent_nets=[],
agent_counts_dict={0: 1}
)
rc = RunContext(overrides={"model.policy": policy_composer, **es_overrides}, runner="dev", configuration="test")
rc.train(n_epochs=1)
rc = RunContext(algorithm=alg_config, overrides=ac_overrides, runner="dev", configuration="test")
rc.train(n_epochs=1)
rc.train()
# Rollout
# -------
obs = env.reset()
for i in range(10):
action = rc.compute_action(obs)
obs, rewards, dones, info = env.step(action)
# Evaluation
# ----------
env.reset()
evaluator = RolloutEvaluator(
# Environment has to be have statistics logging capabilities for RolloutEvaluator.
eval_env=LogStatsWrapper.wrap(env, logging_prefix="eval"),
n_episodes=1,
model_selection=None
)
evaluator.evaluate(rc.policy)