def test_pong_with_worker(self):
env_spec = dict(
type="openai",
gym_env="PongNoFrameskip-v4",
# The frameskip in the agent config will trigger worker skips, this
# is used for internal env.
frameskip=4,
max_num_noops=30,
episodic_life=False
)
env = OpenAIGymEnv.from_spec(env_spec)
agent_config = config_from_path("configs/backend_performance_dqn_pong.json")
# Test cpu settings for batching here.
agent_config["update_spec"] = None
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
worker = SingleThreadedWorker(
env_spec=env_spec,
agent=agent,
frameskip=1,
preprocessing_spec=agent_config["preprocessing_spec"],
worker_executes_preprocessing=True
)
result = worker.execute_timesteps(1000)
print(result)
def test_multi_gpu_dqn_agent_compilation(self):
"""
Tests if the multi gpu strategy can compile successfully on a multi gpu system, but
also runs on a CPU-only system using fake-GPU logic for testing purposes.
"""
root_logger.setLevel(DEBUG)
agent_config = config_from_path(
"configs/multi_gpu_dqn_for_random_env.json")
environment = RandomEnv.from_spec(self.random_env_spec)
agent = DQNAgent.from_spec(agent_config,
state_space=environment.state_space,
action_space=environment.action_space)
print("Compiled DQN agent on multi-GPU system")
# Do an update from external batch.
batch_size = agent_config["update_spec"]["batch_size"]
external_batch = dict(
states=environment.state_space.sample(size=batch_size),
actions=environment.action_space.sample(size=batch_size),
rewards=np.random.sample(size=batch_size),
terminals=np.random.choice([True, False], size=batch_size),
next_states=environment.state_space.sample(size=batch_size),
importance_weights=np.zeros(shape=(batch_size, )))
agent.update(batch=external_batch)
print("Performed an update from external batch")
def test_readme_example(self):
"""
Tests deterministic functionality of RandomEnv.
"""
from rlgraph.agents import DQNAgent
from rlgraph.environments import OpenAIGymEnv
environment = OpenAIGymEnv('CartPole-v0')
config = config_from_path("../../examples/configs/dqn_cartpole.json")
# Create from .json file or dict, see agent API for all
# possible configuration parameters.
agent = DQNAgent.from_spec(config,
state_space=environment.state_space,
action_space=environment.action_space)
# Get an action, take a step, observe reward.
state = environment.reset()
preprocessed_state, action = agent.get_action(
states=state, extra_returns="preprocessed_states")
# Execute step in environment.
next_state, reward, terminal, info = environment.step(action)
# Observe result.
agent.observe(preprocessed_states=preprocessed_state,
actions=action,
internals=[],
next_states=next_state,
rewards=reward,
terminals=terminal)
# Call update when desired:
loss = agent.update()
def test_double_dueling_dqn_on_cart_pole(self):
"""
Creates a double and dueling DQNAgent and runs it via a Runner on the CartPole Env.
"""
dummy_env = OpenAIGymEnv("CartPole-v0")
agent = DQNAgent.from_spec(
config_from_path("configs/dqn_agent_for_cartpole.json"),
double_q=True,
dueling_q=True,
state_space=dummy_env.state_space,
action_space=dummy_env.action_space,
observe_spec=dict(buffer_size=200),
execution_spec=dict(seed=156),
update_spec=dict(update_interval=4,
batch_size=64,
sync_interval=16),
optimizer_spec=dict(type="adam", learning_rate=0.05),
store_last_q_table=True)
time_steps = 3000
worker = SingleThreadedWorker(
env_spec=lambda: OpenAIGymEnv("CartPole-v0", seed=10),
agent=agent,
render=self.is_windows,
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"], 15)
self.assertGreaterEqual(results["max_episode_reward"], 160.0)
self.assertLessEqual(results["episodes_executed"], 100)
def test_cartpole_with_worker(self):
env = OpenAIGymEnv("CartPole-v0")
agent_config = config_from_path("configs/backend_performance_dqn_cartpole.json")
# Test cpu settings for batching here.
agent_config["update_spec"] = None
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
worker = SingleThreadedWorker(
env_spec=lambda: OpenAIGymEnv("CartPole-v0"),
agent=agent,
frameskip=1,
num_environments=1,
worker_executes_preprocessing=False
)
result = worker.execute_timesteps(1000)
print(result)
def test_act(self):
env = OpenAIGymEnv("Pong-v0", frameskip=4, max_num_noops=30, episodic_life=True)
agent_config = config_from_path("configs/ray_apex_for_pong.json")
if get_backend() == "pytorch":
agent_config["memory_spec"]["type"] = "mem_prioritized_replay"
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
state = env.reset()
action = agent.get_action(state)
print("Component call count = {}".format(Component.call_count))
state_space = env.state_space
count = 200
samples = state_space.sample(count)
start = time.perf_counter()
for s in samples:
action = agent.get_action(s)
end = time.perf_counter() - start
print("Took {} s for {} separate actions, mean = {}".format(end, count, end / count))
# Now instead test 100 batch actions
samples = state_space.sample(count)
start = time.perf_counter()
action = agent.get_action(samples)
end = time.perf_counter() - start
print("Took {} s for {} batched actions.".format(end, count))
profile = Component.call_times
print_call_chain(profile, False, 0.03)
def test_double_dqn_on_2x2_grid_world_single_action_to_container(self):
"""
Tests how dqn solves a mapping of a single integer to multiple actions (as opposed to using container
actions).
"""
# ftj = forward + turn + jump
env_spec = dict(world="2x2",
action_type="ftj",
state_representation="xy+orientation")
agent_config = config_from_path(
"configs/dqn_agent_for_2x2_gridworld_single_to_container.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
action_space = IntBox(0, 18)
agent = DQNAgent.from_spec(agent_config,
huber_loss=True,
double_q=True,
dueling_q=True,
state_space=FloatBox(shape=(4, )),
action_space=action_space,
store_last_q_table=True)
time_steps = 10000
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld.from_spec(env_spec),
agent=agent,
preprocessing_spec=preprocessing_spec,
worker_executes_preprocessing=True,
render=False)
results = worker.execute_timesteps(time_steps, use_exploration=True)
print(results)
def test_dqn_on_cart_pole(self):
"""
Creates a DQNAgent and runs it via a Runner on the CartPole Env.
"""
dummy_env = OpenAIGymEnv("CartPole-v0")
agent = DQNAgent.from_spec(
config_from_path("configs/dqn_agent_for_cartpole.json"),
double_q=False,
dueling_q=False,
state_space=dummy_env.state_space,
action_space=dummy_env.action_space,
execution_spec=dict(seed=15),
update_spec=dict(update_interval=4,
batch_size=24,
sync_interval=64),
optimizer_spec=dict(type="adam", learning_rate=0.05),
store_last_q_table=True)
time_steps = 3000
worker = SingleThreadedWorker(
env_spec=lambda: OpenAIGymEnv("CartPole-v0", seed=15),
agent=agent,
render=self.is_windows,
worker_executes_preprocessing=False)
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"], 25)
self.assertGreaterEqual(results["max_episode_reward"], 100.0)
self.assertLessEqual(results["episodes_executed"], 200)
def test_double_dqn_on_2x2_grid_world(self):
"""
Creates a double DQNAgent and runs it via a Runner on a simple 2x2 GridWorld.
"""
env_spec = dict(world="2x2")
dummy_env = GridWorld.from_spec(env_spec)
agent_config = config_from_path(
"configs/dqn_agent_for_2x2_gridworld.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = DQNAgent.from_spec(
agent_config,
dueling_q=False,
state_space=self.grid_world_2x2_flattened_state_space,
action_space=dummy_env.action_space,
execution_spec=dict(seed=10),
update_spec=dict(update_interval=4,
batch_size=24,
sync_interval=32),
optimizer_spec=dict(type="adam", learning_rate=0.05),
store_last_q_table=True)
time_steps = 1000
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld.from_spec(env_spec),
agent=agent,
preprocessing_spec=preprocessing_spec,
worker_executes_preprocessing=True)
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"], 350)
# 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_dqn_compilation(self):
"""
Tests DQN Agent compilation.
"""
env = OpenAIGymEnv("Pong-v0", frameskip=4, max_num_noops=30, episodic_life=True)
agent_config = config_from_path("configs/dqn_agent_for_pong.json")
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
def test_dqn_compilation(self):
"""
Creates a DQNAgent and runs it via a Runner on an openAI Pong Env.
"""
env = OpenAIGymEnv("Pong-v0", frameskip=4, max_num_noops=30, episodic_life=True)
agent_config = config_from_path("configs/dqn_pytorch_test.json")
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
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_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_double_dueling_dqn_on_cart_pole(self):
"""
Creates a double and dueling DQNAgent and runs it via a Runner on the CartPole Env.
"""
gym_env = "CartPole-v0"
dummy_env = OpenAIGymEnv(gym_env)
config_ = config_from_path("configs/dqn_agent_for_cartpole.json")
# Add dueling config to agent.
config_["policy_spec"] = {
"units_state_value_stream": 3,
"action_adapter_spec": {
"pre_network_spec": [{
"type": "dense",
"units": 3
}]
}
}
agent = DQNAgent.from_spec(config_,
double_q=True,
dueling_q=True,
state_space=dummy_env.state_space,
action_space=dummy_env.action_space,
execution_spec=dict(seed=13),
update_spec=dict(update_interval=4,
batch_size=64,
sync_interval=16),
optimizer_spec=dict(type="adam",
learning_rate=0.01),
store_last_q_table=True)
time_steps = 3000
worker = SingleThreadedWorker(
env_spec=lambda: OpenAIGymEnv(gym_env, seed=10),
agent=agent,
render=self.is_windows,
worker_executes_preprocessing=False)
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"], 25)
self.assertLessEqual(results["episodes_executed"], 150)
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 test_double_dqn_on_2x2_grid_world_with_container_actions(self):
"""
Creates a double DQNAgent and runs it via a Runner on a simple 2x2 GridWorld using container actions.
"""
# ftj = forward + turn + jump
env_spec = dict(world="2x2",
action_type="ftj",
state_representation="xy+orientation")
dummy_env = GridWorld.from_spec(env_spec)
agent_config = config_from_path(
"configs/dqn_agent_for_2x2_gridworld_with_container_actions.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = DQNAgent.from_spec(agent_config,
double_q=True,
dueling_q=False,
state_space=FloatBox(shape=(4, )),
action_space=dummy_env.action_space,
execution_spec=dict(seed=15),
store_last_q_table=True)
time_steps = 10000
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld.from_spec(env_spec),
agent=agent,
preprocessing_spec=preprocessing_spec,
worker_executes_preprocessing=True,
render=False)
results = worker.execute_timesteps(time_steps, use_exploration=True)
print("LAST q-table:\n{}".format(agent.last_q_table))
self.assertEqual(results["timesteps_executed"], time_steps)
self.assertEqual(results["env_frames"], time_steps)
self.assertGreaterEqual(results["mean_episode_reward"], -7)
self.assertGreaterEqual(results["max_episode_reward"], -1.0)
self.assertLessEqual(results["episodes_executed"], time_steps / 3)
# Check q-table for correct values.
expected_q_values_per_state = {
(0., 0., -1., 0.): {
"forward": (-5.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 0., 1., 0.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 0., 0., -1.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 0., 0., 1.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 1., -1., 0.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 1., 1., 0.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 1., 0., -1.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
(0., 1., 0., 1.): {
"forward": (0.0, -1.0, -1.0),
"jump": (0.0, -1.0)
},
}
for state, q_values_forward, q_values_jump in zip(
agent.last_q_table["states"],
agent.last_q_table["q_values"]["forward"],
agent.last_q_table["q_values"]["jump"]):
state, q_values_forward, q_values_jump = tuple(state), tuple(
q_values_forward), tuple(q_values_jump)
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_forward,
expected_q_values_per_state[state]["forward"],
decimals=0)
recursive_assert_almost_equal(
q_values_jump,
expected_q_values_per_state[state]["jump"],
decimals=0)
