def test_sac_2x2_grid_world_with_container_actions(self):
"""
Creates a SAC agent 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/sac_agent_for_2x2_gridworld_with_container_actions.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = SACAgent.from_spec(
agent_config,
state_space=FloatBox(shape=(4,)),
action_space=dummy_env.action_space,
)
time_steps = 10000
worker = SingleThreadedWorker(
env_spec=lambda: GridWorld.from_spec(env_spec),
agent=agent,
preprocessing_spec=preprocessing_spec,
worker_executes_preprocessing=False,
render=False
)
results = worker.execute_timesteps(time_steps, use_exploration=True)
print(results)
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_2x2_grid_world_using_flow_methods(self):
"""
Tests a minimalistic 2x2 GridWorld.
"""
env = GridWorld(world="2x2")
# Simple test runs with fixed actions.
# X=player's position
s, r, t = env.step_flow(2) # down: [" H", "XG"]
self.assertTrue(s == 1)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t = env.step_flow(1) # right: [" H", " X"]
self.assertTrue(s == 0)
self.assertTrue(r == 1.0)
self.assertTrue(t)
s, r, t = env.step_flow(1) # right: [" X", " G"] -> in the hole
self.assertTrue(s == 0)
self.assertTrue(r == -5.0)
self.assertTrue(t)
# Run against a wall.
s, r, t = env.step_flow(3) # left: ["XH", " G"]
self.assertTrue(s == 0)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t = env.step_flow(2) # down: [" H", "XG"]
self.assertTrue(s == 1)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t = env.step_flow(0) # up: ["XH", " G"]
self.assertTrue(s == 0)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
def test_ppo_agent_faulty_op_visualization(self):
"""
Creates a PPOAgent with a badly connected network and visualizes the root component.
"""
agent_config = config_from_path(
"configs/ppo_agent_for_2x2_gridworld.json")
# Sabotage the NN.
agent_config["network_spec"] = [{
"type": "dense",
"units": 10
}, {
"type": "embedding",
"embed_dim": 3,
"vocab_size": 4
}]
env = GridWorld(world="2x2")
# Build Agent and hence trigger the Space error.
try:
ppo_agent = PPOAgent.from_spec(
agent_config,
state_space=GridWorld.grid_world_2x2_flattened_state_space,
action_space=env.action_space)
except RLGraphSpaceError as e:
print("Seeing expected RLGraphSpaceError ({}). Test ok.".format(e))
else:
raise RLGraphError(
"Not seeing expected RLGraphSpaceError with faulty input Space to embed layer of PPO!"
)
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_ppo_on_2x2_grid_world(self):
"""
Creates a PPO Agent and runs it via a Runner on the 2x2 Grid World Env.
"""
env = GridWorld(world="2x2")
agent = PPOAgent.from_spec(
config_from_path("configs/ppo_agent_for_2x2_gridworld.json"),
state_space=GridWorld.grid_world_2x2_flattened_state_space,
action_space=env.action_space,
execution_spec=dict(seed=15),
)
time_steps = 3000
worker = SingleThreadedWorker(
env_spec=lambda: env,
agent=agent,
worker_executes_preprocessing=True,
preprocessing_spec=GridWorld.grid_world_2x2_preprocessing_spec
)
results = worker.execute_timesteps(time_steps, use_exploration=True)
print(results)
# Assume we have learned something.
self.assertGreater(results["mean_episode_reward"], -0.2)
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_ppo_agent_visualization(self):
"""
Creates a PPOAgent and visualizes meta-graph (no APIs) and the NN-component.
"""
env = GridWorld(world="2x2")
env.render()
ppo_agent = PPOAgent.from_spec(
config_from_path("configs/ppo_agent_for_2x2_gridworld.json"),
state_space=GridWorld.grid_world_2x2_flattened_state_space,
action_space=env.action_space)
# Test graphviz component-graph drawing.
draw_meta_graph(ppo_agent.root_component,
output=rlgraph_dir + "/ppo.gv",
apis=False,
graph_fns=False)
self.assertTrue(os.path.isfile(rlgraph_dir + "/ppo.gv"))
# Test graphviz component-graph w/ API drawing (only the Policy component).
draw_meta_graph(ppo_agent.policy.neural_network,
output=rlgraph_dir + "/ppo_nn.gv",
apis=True)
self.assertTrue(os.path.isfile(rlgraph_dir + "/ppo_nn.gv"))
def test_impala_single_agent_compilation(self):
"""
Tests IMPALA agent compilation (single-node mode).
"""
env = GridWorld("2x2")
agent = IMPALAAgent.from_spec(
config_from_path("configs/impala_agent_for_2x2_gridworld.json"),
state_space=env.state_space,
action_space=env.action_space,
update_spec=dict(batch_size=16),
optimizer_spec=dict(type="adam", learning_rate=0.05))
agent.terminate()
print("Compiled IMPALA type=single agent.")
def test_ppo_on_2x2_grid_world_with_container_actions(self):
"""
Creates a PPO agent and runs it via a Runner on a simple 2x2 GridWorld using container actions.
"""
# -----
# |^|H|
# -----
# | |G| ^=start, looking up
# -----
# 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/ppo_agent_for_2x2_gridworld_with_container_actions.json")
preprocessing_spec = agent_config.pop("preprocessing_spec")
agent = PPOAgent.from_spec(agent_config,
state_space=FloatBox(shape=(4, )),
action_space=dummy_env.action_space)
time_steps = 5000
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)
self.assertEqual(results["timesteps_executed"], time_steps)
self.assertEqual(results["env_frames"], time_steps)
self.assertLessEqual(results["episodes_executed"], time_steps)
# Assume we have learned something.
self.assertGreaterEqual(results["mean_episode_reward"], -2.0)
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_impala_single_agent_compilation(self):
"""
Tests IMPALA agent compilation (single-node mode).
"""
return
if get_backend() == "pytorch":
return
env = GridWorld("2x2")
agent = IMPALAAgent.from_spec(
config_from_path("configs/impala_agent_for_2x2_gridworld.json"),
state_space=env.state_space,
action_space=env.action_space,
update_spec=dict(batch_size=16),
optimizer_spec=dict(type="adam", learning_rate=0.05),
# Make session-creation hang in docker.
execution_spec=dict(disable_monitoring=True))
agent.terminate()
print("Compiled {}".format(agent))
def test_impala_on_2x2_grid_world(self):
"""
Creates a single IMPALAAgent and runs it via a simple loop on a 2x2 GridWorld.
"""
env = GridWorld("2x2")
agent = IMPALAAgent.from_spec(
config_from_path("configs/impala_agent_for_2x2_gridworld.json"),
state_space=env.state_space,
action_space=env.action_space,
execution_spec=dict(seed=12),
update_spec=dict(batch_size=16),
optimizer_spec=dict(type="adam", learning_rate=0.05))
learn_updates = 50
for i in range(learn_updates):
ret = agent.update()
mean_return = self._calc_mean_return(ret)
print("i={} Loss={:.4} Avg-reward={:.2}".format(
i, float(ret[1]), mean_return))
# Assume we have learned something.
self.assertGreater(mean_return, -0.1)
# Check the last action probs for the 2 valid next_states (start (after a reset) and one below start).
action_probs = ret[3]["action_probs"].reshape((80, 4))
next_states = ret[3]["states"][:, 1:].reshape((80, ))
for s_, probs in zip(next_states, action_probs):
# Start state:
# - Assume we picked "right" in state=1 (in order to step into goal state).
# - OR we picked "up" or "left" in state=0 (unlikely, but possible).
if s_ == 0:
recursive_assert_almost_equal(probs[0], 0.0, decimals=2)
self.assertTrue(probs[1] > 0.99 or probs[2] > 0.99)
recursive_assert_almost_equal(probs[3], 0.0, decimals=2)
# One below start:
# - Assume we picked "down" in start state with very large probability.
# - OR we picked "left" or "down" in state=1 (unlikely, but possible).
elif s_ == 1:
recursive_assert_almost_equal(probs[0], 0.0, decimals=2)
self.assertTrue(probs[1] > 0.99 or probs[2] > 0.99)
recursive_assert_almost_equal(probs[3], 0.0, decimals=2)
agent.terminate()
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_impala_on_2x2_grid_world(self):
"""
Creates a single IMPALAAgent and runs it via the IMPALAWorker on a simple 2x2 GridWorld.
"""
env = GridWorld("2x2")
agent = IMPALAAgent.from_spec(
config_from_path("configs/impala_agent_for_2x2_gridworld.json"),
state_space=env.state_space,
action_space=env.action_space,
execution_spec=dict(seed=12),
update_spec=dict(update_interval=4, batch_size=16),
optimizer_spec=dict(type="adam", learning_rate=0.05),
)
learn_updates = 1000
# Setup the queue runner.
agent.call_api_method("setup_queue_runner")
for _ in range(learn_updates):
agent.update()
#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"], -3.5)
#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_weights_getting_setting(self):
"""
Tests getting and setting of the Agent's weights.
"""
env = GridWorld(world="2x2")
agent = Agent.from_spec(
config_from_path("configs/dqn_agent_for_functionality_test.json"),
state_space=env.state_space,
action_space=env.action_space)
weights = agent.get_weights()
new_weights = {}
for key, weight in weights["policy_weights"].items():
new_weights[key] = weight + 0.01
agent.set_weights(new_weights)
new_actual_weights = agent.get_weights()
recursive_assert_almost_equal(new_actual_weights["policy_weights"],
new_weights)
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)
def test_long_chain_grid_world(self):
"""
Tests a minimalistic long-chain GridWorld.
"""
env = GridWorld(world="long-chain")
# Simple test runs with fixed actions.
# X=player's position
s = env.reset() # ["X G"]
self.assertTrue(s == 33)
s, r, t, _ = env.step(2) # down: ["X G"]
self.assertTrue(s == 33)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(1) # right: ["SX G"]
self.assertTrue(s == 34)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
env.reset() # ["X G"]
# Right, left, down, up, right -> Move one right each iteration.
for x in range(20):
s, r, t, _ = env.step(1)
self.assertTrue(s == x + 33 + 1)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(3)
self.assertTrue(s == x + 33)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(2)
self.assertTrue(s == x + 33)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(0)
self.assertTrue(s == x + 33)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(1)
self.assertTrue(s == x + 33 + 1)
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
def test_4x4_grid_world_with_container_actions(self):
"""
Tests a 4x4 GridWorld using forward+turn+jump container actions.
"""
env = GridWorld(world="4x4", action_type="ftj", state_representation="xy+orientation")
# Simple test runs with fixed actions.
# Fall into hole.
s = env.reset() # [0, 0, 0] (x, y, orientation)
recursive_assert_almost_equal(s, [0, 0, 0, 1])
s, r, t, _ = env.step(dict(turn=2, forward=2)) # turn=2 (right), move=2 (forward), jump=0
recursive_assert_almost_equal(s, [1, 0, 1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=2, forward=1)) # turn=2 (right), move=1 (stay), jump=0
recursive_assert_almost_equal(s, [1, 0, 0, -1])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=1, forward=2)) # turn=1 (no turn), move=2 (forward), jump=0
recursive_assert_almost_equal(s, [1, 1, 0, -1])
self.assertTrue(r == -5.0)
self.assertTrue(t)
# Jump quite a lot and reach goal.
env.reset() # [0, 0, 0] (x, y, orientation)
s, r, t, _ = env.step(dict(turn=2, forward=1))
recursive_assert_almost_equal(s, [0, 0, 1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=1, forward=1, jump=1))
recursive_assert_almost_equal(s, [2, 0, 1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=2, forward=2))
recursive_assert_almost_equal(s, [2, 1, 0, -1])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=1, forward=2, jump=1))
recursive_assert_almost_equal(s, [2, 3, 0, -1])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=2, forward=0))
recursive_assert_almost_equal(s, [3, 3, -1, 0])
self.assertTrue(r == 1.0)
self.assertTrue(t)
# Run against a wall.
env.reset() # [0, 0, 0] (x, y, orientation)
s, r, t, _ = env.step(dict(turn=1, forward=0))
recursive_assert_almost_equal(s, [0, 1, 0, 1])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=0, forward=2))
recursive_assert_almost_equal(s, [0, 1, -1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
# Jump over a hole (no reset).
s, r, t, _ = env.step(dict(turn=2, forward=1)) # turn around
s, r, t, _ = env.step(dict(turn=2, forward=1))
recursive_assert_almost_equal(s, [0, 1, 1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
s, r, t, _ = env.step(dict(turn=1, forward=1, jump=1))
recursive_assert_almost_equal(s, [2, 1, 1, 0])
recursive_assert_almost_equal(r, -0.1)
self.assertTrue(not t)
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)
def test_2x2_grid_world(self):
"""
Tests a minimalistic 2x2 GridWorld.
"""
env = GridWorld(world="2x2")
# Make everything deterministic.
env.seed(55)
# Simple test runs with fixed actions.
# X=player's position
s = env.reset() # ["XH", " G"] X=player's position
self.assertTrue(s == 0)
s, r, t, _ = env.step(2) # down: [" H", "XG"]
self.assertTrue(s == 1)
self.assertTrue(r == -1.0)
self.assertTrue(t is False)
s, r, t, _ = env.step(1) # right: [" H", " X"]
self.assertTrue(s == 3)
self.assertTrue(r == 1.0)
self.assertTrue(t is True)
env.reset() # ["XH", " G"] X=player's position
s, r, t, _ = env.step(1) # right: [" X", " G"] -> in the hole
self.assertTrue(s == 2)
self.assertTrue(r == -5.0)
self.assertTrue(t is True)
# Run against a wall.
env.reset() # ["XH", " G"] X=player's position
s, r, t, _ = env.step(3) # left: ["XH", " G"]
self.assertTrue(s == 0)
self.assertTrue(r == -1.0)
self.assertTrue(t is False)
s, r, t, _ = env.step(2) # down: [" H", "XG"]
self.assertTrue(s == 1)
self.assertTrue(r == -1.0)
self.assertTrue(t is False)
s, r, t, _ = env.step(0) # up: ["XH", " G"]
self.assertTrue(s == 0)
self.assertTrue(r == -1.0)
self.assertTrue(t is False)