def test_multi_gpu_ppo_agent_learning_test_gridworld_2x2(self):
"""
Tests if the multi gpu strategy can learn successfully on a multi gpu system, but
also runs on a CPU-only system using fake-GPU logic for testing purposes.
"""
env_spec = dict(type="grid-world", world="2x2")
dummy_env = GridWorld.from_spec(env_spec)
agent_config = config_from_path(
"configs/multi_gpu_ppo_for_2x2_gridworld.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = PPOAgent.from_spec(
agent_config,
state_space=self.grid_world_2x2_flattened_state_space,
action_space=dummy_env.action_space,
)
time_steps = 10000
worker = SingleThreadedWorker(env_spec=env_spec,
agent=agent,
worker_executes_preprocessing=True,
preprocessing_spec=preprocessing_spec)
results = worker.execute_timesteps(time_steps, use_exploration=True)
# Assume we have learned something.
# TODO: This test needs more tuning. -1.0 is not great for the 2x2 grid world.
self.assertGreater(results["mean_episode_reward"], -1.0)
def test_multi_gpu_dqn_agent_learning_test_gridworld_2x2(self):
"""
Tests if the multi gpu strategy can learn successfully on a multi gpu system, but
also runs on a CPU-only system using fake-GPU logic for testing purposes.
"""
env_spec = dict(type="grid-world", world="2x2")
dummy_env = GridWorld.from_spec(env_spec)
agent_config = config_from_path(
"configs/multi_gpu_dqn_for_2x2_gridworld.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = DQNAgent.from_spec(
agent_config,
state_space=self.grid_world_2x2_flattened_state_space,
action_space=dummy_env.action_space,
)
time_steps = 1000
worker = SingleThreadedWorker(env_spec=env_spec,
agent=agent,
worker_executes_preprocessing=True,
preprocessing_spec=preprocessing_spec)
results = worker.execute_timesteps(time_steps, use_exploration=True)
# Marge q-tables of all four GPUs:
agent.last_q_table["q_values"] = agent.last_q_table[
"q_values"].reshape((48, 4))
print("STATES:\n{}".format(agent.last_q_table["states"]))
print("\n\nQ(s,a)-VALUES:\n{}".format(
np.round_(agent.last_q_table["q_values"], decimals=2)))
self.assertEqual(results["timesteps_executed"], time_steps)
self.assertEqual(results["env_frames"], time_steps)
self.assertGreaterEqual(results["mean_episode_reward"], -4.5)
self.assertGreaterEqual(results["max_episode_reward"], 0.0)
self.assertLessEqual(results["episodes_executed"], time_steps / 2)
# Check q-table for correct values.
expected_q_values_per_state = {
(1.0, 0, 0, 0): (-1, -5, 0, -1),
(0, 1.0, 0, 0): (-1, 1, 0, 0)
}
for state, q_values in zip(agent.last_q_table["states"],
agent.last_q_table["q_values"]):
state, q_values = tuple(state), tuple(q_values)
assert state in expected_q_values_per_state, \
"ERROR: state '{}' not expected in q-table as it's a terminal state!".format(state)
recursive_assert_almost_equal(q_values,
expected_q_values_per_state[state],
decimals=0)
def test_episodes(self):
"""
Simply tests if episode execution loop works and returns a result.
"""
agent = RandomAgent(action_space=self.environment.action_space,
state_space=self.environment.state_space)
worker = SingleThreadedWorker(env_spec=lambda: self.environment,
agent=agent,
frameskip=1,
worker_executes_preprocessing=False)
result = worker.execute_episodes(5, max_timesteps_per_episode=10)
# Max 5 * 10.
self.assertLessEqual(result['timesteps_executed'], 50)
self.assertEqual(result['episodes_executed'], 5)
self.assertLessEqual(result['env_frames'], 50)
self.assertGreaterEqual(result['runtime'], 0.0)
def test_multi_gpu_dqn_agent_learning_test_gridworld_2x2(self):
"""
Tests if the multi gpu strategy can learn successfully on a multi gpu system.
THIS TEST REQUIRES A MULTI GPU SYSTEM.
"""
#root_logger.setLevel(DEBUG) # test
env = GridWorld("2x2")
agent = DQNAgent.from_spec(
config_from_path("configs/multi_gpu_dqn_for_2x2_gridworld.json"),
dueling_q=False,
state_space=env.state_space,
action_space=env.action_space,
observe_spec=dict(buffer_size=100),
# Rule of thumb for multi-GPU (with n GPUs): n-fold batch-size and learning rate w/ respect to 1 GPU.
update_spec=dict(update_interval=4, batch_size=48, sync_interval=32),
optimizer_spec=dict(type="adam", learning_rate=0.15),
store_last_q_table=True
)
time_steps = 400
worker = SingleThreadedWorker(env_spec=lambda: env, agent=agent, worker_executes_preprocessing=False)
results = worker.execute_timesteps(time_steps, use_exploration=True)
print("STATES:\n{}".format(agent.last_q_table["states"]))
print("\n\nQ(s,a)-VALUES:\n{}".format(np.round_(agent.last_q_table["q_values"], decimals=2)))
self.assertEqual(results["timesteps_executed"], time_steps)
self.assertEqual(results["env_frames"], time_steps)
self.assertGreaterEqual(results["mean_episode_reward"], -4.5)
self.assertGreaterEqual(results["max_episode_reward"], 0.0)
self.assertLessEqual(results["episodes_executed"], 250)
# Check q-table for correct values.
expected_q_values_per_state = {
(1.0, 0, 0, 0): (-1, -5, 0, -1),
(0, 1.0, 0, 0): (-1, 1, 0, 0)
}
for state, q_values in zip(agent.last_q_table["states"], agent.last_q_table["q_values"]):
state, q_values = tuple(state), tuple(q_values)
assert state in expected_q_values_per_state, \
"ERROR: state '{}' not expected in q-table as it's a terminal state!".format(state)
recursive_assert_almost_equal(q_values, expected_q_values_per_state[state], decimals=0)
def test_ppo_on_container_state_and_action_spaces_and_very_large_rewards(
self):
"""
Tests stability of PPO on an extreme env producing strange container states and large rewards and requiring
container actions.
"""
env = RandomEnv(
state_space=Dict(
{"F_position": FloatBox(shape=(2, ), low=0.01, high=0.02)}),
action_space=Dict({
"F_direction_low-1.0_high1.0":
FloatBox(shape=(), low=-1.0, high=1.0),
"F_forward_direction_low-1.0_high1.0":
FloatBox(shape=(), low=-1.0, high=1.0),
"B_jump":
BoolBox()
}),
reward_space=FloatBox(low=-1000.0,
high=-100000.0), # hugely negative rewards
terminal_prob=0.0000001)
agent_config = config_from_path(
"configs/ppo_agent_for_random_env_with_container_spaces.json")
agent = PPOAgent.from_spec(agent_config,
state_space=env.state_space,
action_space=env.action_space)
worker = SingleThreadedWorker(
env_spec=lambda: env,
agent=agent,
preprocessing_spec=None,
worker_executes_preprocessing=True,
#episode_finish_callback=lambda episode_return, duration, timesteps, env_num:
#print("episode return {}; steps={}".format(episode_return, timesteps))
)
results = worker.execute_timesteps(num_timesteps=int(1e6),
use_exploration=True)
print(results)
def test_prioritized_replay_atari_throughput(self):
"""
Tests throughput on standard Atari environments using the prioritized replay memory.
"""
agent = DQNAgent(
states_spec=self.env.state_space,
action_spec=self.env.action_space,
network_spec=self.network,
memory_spec=dict(
type='prioritized',
capacity=100000,
next_states=True
)
)
worker = SingleThreadedWorker(
env_spec=lambda: self.env,
agent=agent,
frameskip=1
)
result = worker.execute_timesteps(num_timesteps=1000000, use_exploration=True)
print('Agent throughput = {} ops/s'.format(result['ops_per_second']))
print('Environment throughput = {} frames/s'.format(result['env_frames_per_second']))
def test_multi_gpu_dqn_agent_learning_test_gridworld_2x2(self):
"""
Tests if the multi gpu strategy can learn successfully on a multi gpu system, but
also runs on a CPU-only system using fake-GPU logic for testing purposes.
"""
env_spec = dict(type="grid-world", world="2x2")
dummy_env = GridWorld.from_spec(env_spec)
agent_config = config_from_path(
"configs/multi_gpu_dqn_for_2x2_gridworld.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = DQNAgent.from_spec(
agent_config,
state_space=self.grid_world_2x2_flattened_state_space,
action_space=dummy_env.action_space,
)
time_steps = 2000
worker = SingleThreadedWorker(env_spec=env_spec,
agent=agent,
worker_executes_preprocessing=True,
preprocessing_spec=preprocessing_spec)
results = worker.execute_timesteps(time_steps, use_exploration=True)
self.assertEqual(results["timesteps_executed"], time_steps)
self.assertEqual(results["env_frames"], time_steps)
self.assertGreaterEqual(results["mean_episode_reward"], -4.5)
self.assertGreaterEqual(results["max_episode_reward"], 0.0)
self.assertLessEqual(results["episodes_executed"], time_steps / 2)
# Check all learnt Q-values.
q_values = agent.graph_executor.execute(
("get_q_values", one_hot(np.array([0, 1]), depth=4)))[:]
recursive_assert_almost_equal(q_values[0], (0.8, -5, 0.9, 0.8),
decimals=1)
recursive_assert_almost_equal(q_values[1], (0.8, 1.0, 0.9, 0.9),
decimals=1)
def run_experiment(self, environment, experiment_num=0):
environment = RLgraphEnvironmentWrapper(environment)
environment.add_episode_end_callback(self.episode_finished,
environment,
runner_id=1)
config = copy(self.config)
max_episodes = config.pop('max_episodes', None)
max_timesteps = config.pop('max_timesteps', None)
max_episode_timesteps = config.pop('max_episode_timesteps')
agent = Agent.from_spec(
spec=config,
state_space=environment.state_space,
action_space=environment.action_space,
)
if experiment_num == 0 and self.load_model_file:
logging.info("Loading model data from file: {}".format(
self.load_model))
agent.load_model(self.load_model_file)
runner = SingleThreadedWorker(agent=agent, environment=environment)
environment.reset()
agent.reset_buffers()
if max_timesteps:
runner.execute_timesteps(
num_timesteps=max_timesteps,
max_timesteps_per_episode=max_episode_timesteps)
else:
runner.execute_episodes(
num_episodes=max_episodes,
max_timesteps_per_episode=max_episode_timesteps)
return dict(initial_reset_time=0,
episode_rewards=runner.episode_rewards,
episode_timesteps=runner.episode_steps,
episode_end_times=runner.episode_durations)
def test_dqn_functionality(self):
"""
Creates a DQNAgent and runs it for a few steps in a GridWorld to vigorously test
all steps of the learning process.
"""
env = GridWorld(world="2x2", save_mode=True) # no holes, just fire
agent = Agent.from_spec( # type: DQNAgent
config_from_path("configs/dqn_agent_for_functionality_test.json"),
double_q=True,
dueling_q=True,
state_space=env.state_space,
action_space=env.action_space,
discount=0.95)
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld(world="2x2", save_mode=True),
agent=agent)
test = AgentTest(worker=worker)
# Helper python DQNLossFunc object.
loss_func = DQNLossFunction(backend="python",
double_q=True,
discount=agent.discount)
loss_func.when_input_complete(input_spaces=dict(loss_per_item=[
spaces.FloatBox(shape=(4, ), add_batch_rank=True),
spaces.IntBox(4, add_batch_rank=True),
spaces.FloatBox(add_batch_rank=True),
spaces.BoolBox(add_batch_rank=True),
spaces.FloatBox(shape=(4, ), add_batch_rank=True),
spaces.FloatBox(shape=(4, ), add_batch_rank=True)
]),
action_space=env.action_space)
matrix1_qnet = np.array([[0.9] * 2] * 4)
matrix2_qnet = np.array([[0.8] * 5] * 2)
matrix1_target_net = np.array([[0.9] * 2] * 4)
matrix2_target_net = np.array([[0.8] * 5] * 2)
a = self._calculate_action(0, matrix1_qnet, matrix2_qnet)
# 1st step -> Expect insert into python-buffer.
# action: up (0)
test.step(1, reset=True)
# Environment's new state.
test.check_env("state", 0)
# Agent's buffer.
test.check_agent("states_buffer", [[1.0, 0.0, 0.0, 0.0]],
key_or_index="env_0") # <- prev state (preprocessed)
test.check_agent("actions_buffer", [a], key_or_index="env_0")
test.check_agent("rewards_buffer", [-1.0], key_or_index="env_0")
test.check_agent("terminals_buffer", [False], key_or_index="env_0")
# Memory contents.
test.check_var("replay-memory/index", 0)
test.check_var("replay-memory/size", 0)
test.check_var("replay-memory/memory/states",
np.array([[0] * 4] * agent.memory.capacity))
test.check_var("replay-memory/memory/actions",
np.array([0] * agent.memory.capacity))
test.check_var("replay-memory/memory/rewards",
np.array([0] * agent.memory.capacity))
test.check_var("replay-memory/memory/terminals",
np.array([False] * agent.memory.capacity))
# Check policy and target-policy weights (should be the same).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
# 2nd step -> expect insert into memory (and python buffer should be empty again).
# action: up (0)
# Also check the policy and target policy values (Should be equal at this point).
test.step(1)
test.check_env("state", 0)
test.check_agent("states_buffer", [], key_or_index="env_0")
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_var("replay-memory/index", 2)
test.check_var("replay-memory/size", 2)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 2)))
test.check_var("replay-memory/memory/actions",
np.array([0, 0] + [0] * (agent.memory.capacity - 2)))
test.check_var(
"replay-memory/memory/rewards",
np.array([-1.0, -1.0] + [0.0] * (agent.memory.capacity - 2)))
test.check_var(
"replay-memory/memory/terminals",
np.array([False, True] + [False] * (agent.memory.capacity - 2)))
# Check policy and target-policy weights (should be the same).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_qnet)
# 3rd and 4th step -> expect another insert into memory (and python buffer should be empty again).
# actions: down (2), up (0) <- exploring is True = more random actions
# Expect an update to the policy variables (leave target as is (no sync yet)).
test.step(2, use_exploration=True)
test.check_env("state", 0)
test.check_agent("states_buffer", [], key_or_index="env_0")
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_var("replay-memory/index", 4)
test.check_var("replay-memory/size", 4)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 3 + [[0.0, 1.0, 0.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/actions",
np.array([0, 0, 2, 0] + [0] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/rewards",
np.array([-1.0] * 4 + # + [-3.0] +
[0.0] * (agent.memory.capacity - 4)))
test.check_var(
"replay-memory/memory/terminals",
np.array([False, True] * 2 + [False] *
(agent.memory.capacity - 4)))
# Get the latest memory batch.
expected_batch = dict(states=np.array([[1.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0]]),
actions=np.array([0, 1]),
rewards=np.array([-1.0, -3.0]),
terminals=np.array([False, True]),
next_states=np.array([[1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0]]))
test.check_agent("last_memory_batch", expected_batch)
# Calculate the weight updates and check against actually update weights by the AgentDQN.
mat_updated = self._helper_update_matrix(expected_batch, matrix1_qnet,
matrix2_qnet,
matrix1_target_net,
matrix2_target_net, agent,
loss_func)
# Check policy and target-policy weights (policy should be updated now).
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
mat_updated[0],
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
mat_updated[1],
decimals=4)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_target_net)
matrix1_qnet = mat_updated[0]
matrix2_qnet = mat_updated[1]
# 5th step -> Another buffer update check.
# action: down (2) (weights have been updated -> different actions)
test.step(1)
test.check_env("state", 3)
test.check_agent(
"states_buffer", [], key_or_index="env_0"
) # <- all empty b/c we reached end of episode (buffer gets force-flushed)
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index", 5)
test.check_var("replay-memory/size", 5)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[0.0, 0.0, 0.0, 0.0]] * (agent.memory.capacity - 5)))
test.check_var("replay-memory/memory/actions",
np.array([0, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0] * 3 + [-3.0, 1.0, 0.0]))
test.check_var("replay-memory/memory/terminals",
np.array([False, True] * 2 + [True, False]))
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/action-adapter-0/action-network/action-layer/dense/kernel",
mat_updated[1],
decimals=4)
test.check_var(
"target-policy/action-adapter-0/action-network/action-layer/dense/kernel",
matrix2_target_net)
# 6th/7th step (with exploration enabled) -> Another buffer update check.
# action: up, down (0, 2)
test.step(2, use_exploration=True)
test.check_env("state", 1)
test.check_agent(
"states_buffer", [], key_or_index="env_0"
) # <- all empty again; flushed after 6th step (when buffer was full).
test.check_agent("actions_buffer", [], key_or_index="env_0")
test.check_agent("rewards_buffer", [], key_or_index="env_0")
test.check_agent("terminals_buffer", [], key_or_index="env_0")
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index",
1) # index has been rolled over (memory capacity is 6)
test.check_var("replay-memory/size", 6)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[1.0, 0.0, 0.0, 0.0]]))
test.check_var("replay-memory/memory/actions",
np.array([2, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0] * 3 + [-3.0, 1.0, -1.0]))
test.check_var("replay-memory/memory/terminals",
np.array([True, True, False, True, True, False]))
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=4)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_target_net)
test.check_var(
"dueling-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_qnet,
decimals=4)
test.check_var(
"target-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_target_net)
# 8th step -> Another buffer update check and weights update and sync.
# action: down (2)
test.step(1)
test.check_env("state", 1)
test.check_agent("states_buffer", [1], key_or_index="env_0")
test.check_agent("actions_buffer", [2], key_or_index="env_0")
test.check_agent("rewards_buffer", [-1.0], key_or_index="env_0")
test.check_agent("terminals_buffer", [False], key_or_index="env_0")
expected_batch = dict(
states=np.array([[1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]]),
actions=np.array([0, 1]),
rewards=np.array([-1.0, -3.0]),
terminals=np.array([True, True]),
next_states=np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]])
# TODO: <- This is wrong and must be fixed
# (next-state of first item is from a previous insert and unrelated to first item)
)
test.check_agent("last_memory_batch", expected_batch)
test.check_var("replay-memory/index", 1)
test.check_var("replay-memory/size", 6)
test.check_var(
"replay-memory/memory/states",
np.array([[1.0, 0.0, 0.0, 0.0]] * 4 + [[0.0, 0.0, 1.0, 0.0]] +
[[1.0, 0.0, 0.0, 0.0]]))
test.check_var("replay-memory/memory/actions",
np.array([2, 0, 0, 1, 2, 0]))
test.check_var("replay-memory/memory/rewards",
np.array([-1.0, -1.0, -1.0, -3.0, 1.0, -1.0]))
test.check_var("replay-memory/memory/terminals",
np.array([True, True, False, True, True, False]))
# Assume that the sync happens first (matrices are already the same when updating).
mat_updated = self._helper_update_matrix(expected_batch, matrix1_qnet,
matrix2_qnet, matrix1_qnet,
matrix2_qnet, agent,
loss_func)
# Now target-net should be again 1 step behind policy-net.
test.check_var("dueling-policy/neural-network/hidden/dense/kernel",
mat_updated[0],
decimals=2)
test.check_var("target-policy/neural-network/hidden/dense/kernel",
matrix1_qnet,
decimals=2) # again: old matrix
test.check_var(
"dueling-policy/dueling-action-adapter/action-layer/dense/kernel",
mat_updated[1],
decimals=2)
test.check_var(
"target-policy/dueling-action-adapter/action-layer/dense/kernel",
matrix2_qnet,
decimals=2)