def test_dddqn_n_step_memory_insertion_n_step_samples_only(self):
"""
Tests the n-step post-processing and memory-insertions of DDDQN (with the n_step_only option set to True).
"""
# Create an Env object.
env = GridWorld("2x2", actors=1)
# Create a very standard DDQN.
dqn_config = DDDQNConfig.make(
"{}/../configs/dddqn_grid_world_2x2_learning.json".format(
os.path.dirname(__file__)),
n_step=2, # fix n-step to 2, just in case.
gamma=0.5, # fix gamma for unique-memory-checks purposes
epsilon=[1.0, 0.5], # fix epsilon to get lots of random actions.
preprocessor=Preprocessor(lambda inputs_: tf.one_hot(
inputs_, depth=env.actors[0].state_space.num_categories)),
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space)
algo = DDDQN(config=dqn_config, name="my-dddqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run for n ticks, then check memory contents for correct n-step tuples.
for _ in range(5):
env.run(ticks=100, sync=True, render=False)
self._check_2x2_grid_world_mem(algo.memory, n_step_only=True)
env.terminate()
def test_dddqn_learning_on_cart_pole_with_4_actors(self):
# Create an Env object.
env = OpenAIGymEnv("CartPole-v0", actors=4)
# Create a Config.
dqn_config = DDDQNConfig.make(
"{}/../configs/dddqn_cart_pole_learning_n_actors.json".format(os.path.dirname(__file__)),
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space
)
# Create an Algo object.
algo = DDDQN(config=dqn_config, name="my-dqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run and wait for env to complete.
env.run(ticks=2000, sync=True, render=debug.RenderEnvInLearningTests)
# Check last n episode returns.
last_n = 10
mean_last_episodes = np.mean(env.historic_episodes_returns[-last_n:])
print("Avg return over last {} episodes: {}".format(last_n, mean_last_episodes))
self.assertTrue(mean_last_episodes > 160.0)
env.terminate()
def test_dddqn_learning_on_mountain_car_4_actors(self):
# Note: MountainCar is tricky as per its reward function: Hence, we need a quite large episode
# cutoff to solve it with ease.
# With a large enough n-step, the algo should be able to learn the env very quickly after having solved
# it once via randomness.
env = OpenAIGymEnv("MountainCar-v0", actors=4, max_episode_steps=5000)
# Create a DQN2015Config.
dqn_config = DDDQNConfig.make(
"{}/../configs/dddqn_mountain_car_learning_n_actors.json".format(
os.path.dirname(
__file__)), # TODO: filename wrong (num actors)
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space)
# Create an Algo object.
algo = DDDQN(config=dqn_config, name="my-dqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run and wait for env to complete.
env.run(ticks=7000, sync=True, render=debug.RenderEnvInLearningTests)
# Check last n episode returns.
last_n = 10
mean_last_episodes = np.mean(env.historic_episodes_returns[-last_n:])
print("Avg return over last {} episodes: {}".format(
last_n, mean_last_episodes))
self.assertTrue(mean_last_episodes > -200.0)
env.terminate()
def test_dddqn_learning_on_car_racing(self):
# Action-map: Discrete to Continuous, 9 actions.
# 0=noop
# 1=left
# 2=right
# 3=break only
# 4=break and left
# 5=break and right
# 6=gas only
# 7=gas and left
# 8=gas and right
def action_map(a):
b = np.reshape(a, (-1, 1))
return np.where(
#b == 0, [0.0, 0.0, 0.0], np.where(
# b == 1, [-1.0, 0.0, 0.0], np.where(
# b == 2, [1.0, 0.0, 0.0], np.where(
b == 0, [0.0, 0.0, 1.0], np.where(
b == 1, [-1.0, 0.0, 1.0], np.where(
b == 2, [1.0, 0.0, 1.0], np.where(
b == 3, [0.0, 1.0, 0.0], np.where(
b == 4, [-1.0, 1.0, 0.0], [1.0, 1.0, 0.0]
)))))
# Create an Env object.
env = OpenAIGymEnv("CarRacing-v0", actors=1, action_map=action_map)
# Create a DQN2015Config.
config = DDDQNConfig.make(
"{}/../configs/dddqn_car_racing_learning.json".format(os.path.dirname(__file__)),
preprocessor=Preprocessor(
#ImageCrop(x=0, y=0, width=150, height=167),
GrayScale(keepdims=True),
ImageResize(width=84, height=84, interpolation="bilinear"),
lambda inputs_: ((inputs_ / 128) - 1.0).astype(np.float32),
# simple preprocessor: [0,255] to [-1.0,1.0]
Sequence(sequence_length=4, adddim=False)
),
state_space=env.actors[0].state_space,
action_space=Int(6)
)
# Create an Algo object.
algo = DDDQN(config=config, name="my-dddqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run and wait for env to complete.
env.run(ticks=20000, sync=True, render=debug.RenderEnvInLearningTests)
# Check last n episode returns.
n = 10
mean_last_n = np.mean(env.historic_episodes_returns[-n:])
print("Avg return over last {} episodes: {}".format(n, mean_last_n))
self.assertTrue(mean_last_n > 150.0)
env.terminate()
def test_dddqn_learning_on_grid_world_2x2(self):
# Create an Env object.
env = GridWorld("2x2", actors=1)
# Add the preprocessor.
preprocessor = Preprocessor(
lambda inputs_: tf.one_hot(inputs_, depth=env.actors[0].state_space.num_categories)
)
# Create a Config.
dqn_config = DDDQNConfig.make(
"{}/../configs/dddqn_grid_world_2x2_learning.json".format(os.path.dirname(__file__)),
preprocessor=preprocessor,
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space
)
# Create an Algo object.
algo = DDDQN(config=dqn_config, name="my-dddqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run and wait for env to complete.
env.run(ticks=3000, sync=True, render=debug.RenderEnvInLearningTests)
# Check last n episode returns.
n = 10
mean_last_n = np.mean(env.historic_episodes_returns[-n:])
print("Avg return over last {} episodes: {}".format(n, mean_last_n))
self.assertTrue(mean_last_n >= 0.6)
# Check learnt Q-function (using our dueling layer).
a_and_v = algo.Q(one_hot(np.array([0, 0, 0, 0, 1, 1, 1, 1]), depth=4))
q = dueling(a_and_v, np.array([0, 1, 2, 3, 0, 1, 2, 3]))
print(q)
self.assertTrue(q[1] < min(q[2:]) and q[1] < q[0]) # q(s=0,a=right) is the worst
check(q[5], 1.0, atol=0.4) # Q(1,->) is close to 1.0.
#self.assertTrue(q[5] > max(q[:4]) and q[5] > max(q[6:])) # q(s=1,a=right) is the best
#check(q, [0.8, -5.0, 0.9, 0.8, 0.8, 1.0, 0.9, 0.9], decimals=1) # a=up,down,left,right
env.terminate()
def test_dddqn_compilation(self):
"""
Tests the c'tor of DDDQN.
"""
env = OpenAIGymEnv("MsPacman-v0", actors=4)
# Create a Config (for any Atari game).
config = DDDQNConfig.make(
# Breakout should be the same as MsPacman.
"{}/../configs/dddqn_breakout_learning.json".format(
os.path.dirname(__file__)),
memory_capacity=1000,
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space)
dddqn = DDDQN(config)
print("DDDQN built ({}).".format(dddqn))
env.terminate()
def test_dddqn_learning_on_breakout(self):
# Create an Env object.
env = OpenAIGymEnv(
"Breakout-v4", actors=16, fire_after_reset=True, episodic_life=True, max_num_noops_after_reset=8,
frame_skip=(2, 5)
)
preprocessor = Preprocessor(
ImageCrop(x=5, y=29, width=150, height=167),
GrayScale(keepdims=True),
ImageResize(width=84, height=84, interpolation="bilinear"),
lambda inputs_: ((inputs_ / 128) - 1.0).astype(np.float32), # simple preprocessor: [0,255] to [-1.0,1.0]
Sequence(sequence_length=4, adddim=False)
)
# Create a DQN2015Config.
config = DDDQNConfig.make(
"{}/../configs/dddqn_breakout_learning.json".format(os.path.dirname(__file__)),
preprocessor=preprocessor,
state_space=env.actors[0].state_space,
action_space=env.actors[0].action_space
)
# Create an Algo object.
algo = DDDQN(config=config, name="my-dddqn")
# Point actor(s) to the algo.
env.point_all_actors_to_algo(algo)
# Run and wait for env to complete.
env.run(actor_time_steps=10000000, sync=True, render=debug.RenderEnvInLearningTests)
# Check last n episode returns.
n = 10
mean_last_n = np.mean(env.historic_episodes_returns[-n:])
print("Avg return over last {} episodes: {}".format(n, mean_last_n))
self.assertTrue(mean_last_n > 150.0)
env.terminate()