def test_process_trajectory(dummy_config):
brain_params = make_brain_parameters(discrete_action=False,
visual_inputs=0,
vec_obs_size=6)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params.brain_name, brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trajectory = make_fake_trajectory(
length=15,
max_step_complete=True,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that trainer put trajectory in update buffer
assert trainer.update_buffer.num_experiences == 15
# Check that the stats are being collected as episode isn't complete
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent > 0
# Add a terminal trajectory
trajectory = make_fake_trajectory(
length=15,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that the stats are reset as episode is finished
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent == 0
assert trainer.stats_reporter.get_stats_summaries(
"Policy/Extrinsic Reward").num > 0
# Assert we're not just using the default values
assert (trainer.stats_reporter.get_stats_summaries(
"Policy/Extrinsic Reward").mean > 0)
def test_advance(dummy_config):
brain_params = make_brain_parameters(
discrete_action=False, visual_inputs=0, vec_obs_size=6
)
dummy_config.hyperparameters.steps_per_update = 20
dummy_config.hyperparameters.reward_signal_steps_per_update = 20
dummy_config.hyperparameters.buffer_init_steps = 0
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params.brain_name, brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
policy_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trainer.publish_policy_queue(policy_queue)
trajectory = make_fake_trajectory(
length=15,
max_step_complete=True,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that trainer put trajectory in update buffer
assert trainer.update_buffer.num_experiences == 15
# Check that the stats are being collected as episode isn't complete
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent > 0
# Add a terminal trajectory
trajectory = make_fake_trajectory(
length=6,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that the stats are reset as episode is finished
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent == 0
assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0
# Assert we're not just using the default values
assert (
trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").mean > 0
)
# Make sure there is a policy on the queue
policy_queue.get_nowait()
# Add another trajectory. Since this is less than 20 steps total (enough for)
# two updates, there should NOT be a policy on the queue.
trajectory = make_fake_trajectory(
length=5,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
with pytest.raises(AgentManagerQueue.Empty):
policy_queue.get_nowait()
# Call add_policy and check that we update the correct number of times.
# This is to emulate a load from checkpoint.
policy = trainer.create_policy(brain_params.brain_name, brain_params)
policy.get_current_step = lambda: 200
trainer.add_policy(brain_params.brain_name, policy)
trainer.optimizer.update = mock.Mock()
trainer.optimizer.update_reward_signals = mock.Mock()
trainer.optimizer.update_reward_signals.return_value = {}
trainer.optimizer.update.return_value = {}
trajectory_queue.put(trajectory)
trainer.advance()
# Make sure we did exactly 1 update
assert trainer.optimizer.update.call_count == 1
assert trainer.optimizer.update_reward_signals.call_count == 1
def test_advance(dummy_config):
brain_params = make_brain_parameters(discrete_action=False,
visual_inputs=0,
vec_obs_size=6)
dummy_config["output_path"] = "./results/test_trainer_models/TestModel"
dummy_config["steps_per_update"] = 20
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params.brain_name, brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
policy_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trainer.publish_policy_queue(policy_queue)
trajectory = make_fake_trajectory(
length=15,
max_step_complete=True,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that trainer put trajectory in update buffer
assert trainer.update_buffer.num_experiences == 15
# Check that the stats are being collected as episode isn't complete
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent > 0
# Add a terminal trajectory
trajectory = make_fake_trajectory(
length=6,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that the stats are reset as episode is finished
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent == 0
assert trainer.stats_reporter.get_stats_summaries(
"Policy/Extrinsic Reward").num > 0
# Assert we're not just using the default values
assert (trainer.stats_reporter.get_stats_summaries(
"Policy/Extrinsic Reward").mean > 0)
# Make sure there is a policy on the queue
policy_queue.get_nowait()
# Add another trajectory. Since this is less than 20 steps total (enough for)
# two updates, there should NOT be a policy on the queue.
trajectory = make_fake_trajectory(
length=5,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=[2],
is_discrete=False,
)
trajectory_queue.put(trajectory)
trainer.advance()
with pytest.raises(AgentManagerQueue.Empty):
policy_queue.get_nowait()