def test_take_action_returns_nones_on_missing_values():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
policy.evaluate = MagicMock(return_value={})
brain_info_with_agents = BrainInfo([], [], [], agents=["an-agent-id"])
result = policy.get_action(brain_info_with_agents)
assert result == ActionInfo(None, None, None, None, {})
def test_take_action_returns_empty_with_no_agents():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
# Doesn't really matter what this is
dummy_groupspec = AgentGroupSpec([(1, )], "continuous", 1)
no_agent_step = BatchedStepResult.empty(dummy_groupspec)
result = policy.get_action(no_agent_step)
assert result == ActionInfo.empty()
def test_take_action_returns_nones_on_missing_values():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
policy.evaluate = MagicMock(return_value={})
policy.save_memories = MagicMock()
step_with_agents = BatchedStepResult(
[],
np.array([], dtype=np.float32),
np.array([False], dtype=np.bool),
np.array([], dtype=np.bool),
np.array([0]),
None,
)
result = policy.get_action(step_with_agents, worker_id=0)
assert result == ActionInfo(None, None, {}, [0])
def test_take_action_returns_action_info_when_available():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
policy_eval_out = {
"action": np.array([1.0], dtype=np.float32),
"memory_out": np.array([[2.5]], dtype=np.float32),
"value": np.array([1.1], dtype=np.float32),
}
policy.evaluate = MagicMock(return_value=policy_eval_out)
brain_info_with_agents = BrainInfo([], [], [],
agents=["an-agent-id"],
local_done=[False])
result = policy.get_action(brain_info_with_agents)
expected = ActionInfo(policy_eval_out["action"], policy_eval_out["value"],
policy_eval_out)
assert result == expected
def test_take_action_returns_action_info_when_available():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
policy_eval_out = {
"action": np.array([1.0], dtype=np.float32),
"memory_out": np.array([[2.5]], dtype=np.float32),
"value": np.array([1.1], dtype=np.float32),
}
policy.evaluate = MagicMock(return_value=policy_eval_out)
step_with_agents = BatchedStepResult(
[],
np.array([], dtype=np.float32),
np.array([False], dtype=np.bool),
np.array([], dtype=np.bool),
np.array([0]),
None,
)
result = policy.get_action(step_with_agents)
expected = ActionInfo(policy_eval_out["action"], policy_eval_out["value"],
policy_eval_out, [0])
assert result == expected
def test_take_action_returns_empty_with_no_agents():
test_seed = 3
policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
no_agent_brain_info = BrainInfo([], [], [], agents=[])
result = policy.get_action(no_agent_brain_info)
assert result == ActionInfo([], [], None)