def external_brains(self) -> Dict[str, BrainParameters]:
result = {}
for brain_name in self.env.get_agent_groups():
result[brain_name] = group_spec_to_brain_parameters(
brain_name, self.env.get_agent_group_spec(brain_name)
)
return result
def external_brains():
result = {}
for brain_name in env.get_agent_groups():
result[brain_name] = group_spec_to_brain_parameters(
brain_name, env.get_agent_group_spec(brain_name)
)
return result
def external_brains():
result = {}
brain_name = env.spec.id
# for brain_name in env.get_agent_groups():
result[brain_name] = group_spec_to_brain_parameters(
brain_name, {
"observation_shapes": [(env.observation_space, )],
"action_shape": (env.action_scheme.action_space.n, ),
"action_type": 'DISCRETE'
})
return result
def demo_to_buffer(
file_path: str, sequence_length: int
) -> Tuple[BrainParameters, AgentBuffer]:
"""
Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
"""
group_spec, info_action_pair, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(info_action_pair, group_spec, sequence_length)
brain_params = group_spec_to_brain_parameters("DemoBrain", group_spec)
return brain_params, demo_buffer
def test_ppo_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0)
env = UnityEnvironment(" ")
env.reset()
brain_name = env.get_agent_groups()[0]
batched_step = env.get_step_result(brain_name)
brain_params = group_spec_to_brain_parameters(
brain_name, env.get_agent_group_spec(brain_name))
trainer_parameters = dummy_config
model_path = brain_name
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
policy = PPOPolicy(0, brain_params, trainer_parameters, False, False)
run_out = policy.evaluate(batched_step, list(batched_step.agent_id))
assert run_out["action"].shape == (3, 2)
env.close()
def test_ppo_get_value_estimates(mock_communicator, mock_launcher,
dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0)
env = UnityEnvironment(" ")
env.reset()
brain_name = env.get_agent_groups()[0]
brain_info = step_result_to_brain_info(
env.get_step_result(brain_name), env.get_agent_group_spec(brain_name))
brain_params = group_spec_to_brain_parameters(
brain_name, env.get_agent_group_spec(brain_name))
trainer_parameters = dummy_config
model_path = brain_name
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
policy = PPOPolicy(0, brain_params, trainer_parameters, False, False)
run_out = policy.get_value_estimates(brain_info, 0, done=False)
for key, val in run_out.items():
assert type(key) is str
assert type(val) is float
run_out = policy.get_value_estimates(brain_info, 0, done=True)
for key, val in run_out.items():
assert type(key) is str
assert val == 0.0
# Check if we ignore terminal states properly
policy.reward_signals["extrinsic"].use_terminal_states = False
run_out = policy.get_value_estimates(brain_info, 0, done=True)
for key, val in run_out.items():
assert type(key) is str
assert val != 0.0
env.close()
