def setUp(self):
a = np.arange(10)
b = -a
xps = [Experience() for _ in range(5)]
for i, xp in enumerate(xps, start=0):
aa = a + 10 * i
bb = b + 10 * i
xp.remember(aa, bb)
self.sampler = ExperienceSampler(xps)
def test_remember_handles_multiple_arrays(self):
xp = Experience()
a = np.arange(100)
b = a - 10
c = a / 10
xp.remember(a, b, c)
self.assertEqual(len(xp.memoirs), 3)
for source, target in zip(xp.memoirs, [a, b, c]):
self.assertListEqual(source.tolist(), target.tolist())
def test_sampling_considers_explicit_exclusions(self):
exclusion = (3, 6)
excluded_a = []
excluded_b = []
exps = []
for i in range(1, 6):
a = np.arange(10 * i, 10 * (i + 1))
b = a + 100
exps.append(Experience())
exps[-1].remember(a, b, exclude=exclusion)
for e in exclusion:
excluded_a.append(a[e])
excluded_b.append(b[e])
sampler = ExperienceSampler(exps)
states, states_next, sample_b = sampler.sample(-1)
for e_a, e_b in zip(excluded_a, excluded_b):
self.assertNotIn(e_a, states)
self.assertNotIn(e_b, sample_b)
actor.compile(loss="categorical_crossentropy", optimizer=Adam(1e-4))
critic = Sequential([
Dense(16,
activation="relu",
input_shape=input_shape,
kernel_initializer="he_uniform"),
Dense(16, activation="relu", kernel_initializer="he_uniform"),
Dense(1, activation="linear", kernel_initializer="he_uniform")
])
critic.compile(loss="mse", optimizer=Adam(5e-4))
agent = A2C(actor,
critic,
action_space=env.action_space,
memory=Experience(max_length=10000),
discount_factor_gamma=0.98,
entropy_penalty_coef=0.01)
rollout = Rolling(agent, env, config=RolloutConfig(max_steps=300))
test_rollout = Trajectory(agent, gym.make("CartPole-v1"))
rewards = []
actor_loss = []
actor_utility = []
actor_entropy = []
critic_loss = []
for episode in range(1, 1001):
episode_actor_loss = []
episode_actor_utility = []
envs = [FakeEnv() for _ in range(10)]
test_env = FakeEnv()
actor = Sequential([ # 200, 160
Flatten(input_shape=test_env.shape),
Dense(256),
BatchNormalization(),
LeakyReLU(),
Dense(2, activation="softmax")
])
actor.compile(RMSprop(1e-4, rho=0.99), "categorical_crossentropy")
agent = REINFORCE(actor,
2,
Experience(),
discount_factor_gamma=0.99,
state_preprocessor=None)
rollout = MultiTrajectory([agent for _ in range(10)], envs)
test_rollout = Trajectory(agent, test_env)
rewards = deque(maxlen=100)
actor_loss = deque(maxlen=100)
actor_utility = deque(maxlen=100)
actor_entropy = deque(maxlen=100)
critic_loss = deque(maxlen=100)
episode = 0
while 1:
Dense(16, activation="relu"),
Dense(num_actions, activation="softmax")
])
actor.compile(loss="categorical_crossentropy", optimizer=Adam(ACTOR_ADAM_LR))
critic = Sequential([
Dense(16, activation="relu", input_shape=input_shape),
Dense(16, activation="relu"),
Dense(1, activation="linear")
])
critic.compile(loss="mse", optimizer=Adam(CRITIC_ADAM_LR))
agent = PPO(actor,
critic,
action_space=test_env.action_space,
memory=Experience(max_length=EXPERIENCE_SIZE),
reward_discount_factor_gamma=DISCOUNT_FACTOR_GAMMA,
entropy_penalty_coef=ENTROPY_PENALTY_BETA)
rollout = MultiRolling(agent,
envs,
rollout_configs=RolloutConfig(max_steps=MAX_TIMESTEPS))
test_rollout = Trajectory(agent, gym.make("CartPole-v1"))
rewards = []
actor_loss = []
actor_utility = []
actor_kld = []
actor_entropy = []
critic_loss = []
def setUp(self):
self.xp = Experience(max_length=100)
self.sampler = ExperienceSampler(self.xp)
self.a = np.arange(100)
self.b = self.a - 100
self.c = self.a / 10
env = gymic.rwd_scaled_env("LunarLanderContinuous-v2", reward_scale=0.01)
input_shape = env.observation_space.shape
num_actions = env.action_space.shape[0]
actor, critics = mlp.wide_ddpg_actor_critic(input_shape,
output_dim=num_actions,
action_range=2,
num_critics=2,
actor_lr=5e-4,
critic_lr=5e-4)
agent = TD3(actor,
critics,
action_space=spaces.CONTINUOUS,
memory=Experience(max_length=int(1e4)),
discount_factor_gamma=0.99,
action_noise_sigma=0.1,
action_noise_sigma_decay=1.,
action_minima=-2,
action_maxima=2,
target_noise_sigma=0.2,
target_noise_clip=0.5)
rollout = Rolling(agent, env)
test_rollout = Trajectory(agent, env, RolloutConfig(testing_rollout=True))
rollout.fit(episodes=1000,
updates_per_episode=64,
step_per_update=1,
update_batch_size=32,
from trickster.utility import history, visual
from trickster.model import mlp
cfg = MatchConfig(canvas_size=(128, 128),
players_per_side=2,
learning_type=MatchConfig.LEARNING_TYPE_SINGLE_AGENT,
observation_type=MatchConfig.OBSERVATION_TYPE_VECTOR)
env = Match(cfg)
test_env = Match(cfg)
ann = mlp.wide_dueling_q_network(env.observation_space.shape,
env.action_space.n,
adam_lr=1e-4)
experience = Experience(10000)
agent = DoubleDQN(ann,
env.action_space,
experience,
epsilon=1.,
epsilon_decay=1.,
epsilon_min=0.1)
rcfg = RolloutConfig(max_steps=1024, skipframes=2)
training_rollout = Rolling(agent, env, rcfg)
testing_rollout = Trajectory(agent, test_env, rcfg)
print("Filling experience...")
while experience.N < 10000:
training_rollout.roll(steps=32, verbose=0, push_experience=True)
print(f"\r{experience.N/10000:.2%} 10000/{experience.N}", end="")
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
keras.layers.MaxPool2D(), # 10
keras.layers.Conv2D(16, 3, kernel_initializer="he_uniform", padding="same"),
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
keras.layers.MaxPool2D(), # 5
keras.layers.GlobalAveragePooling2D(), # 16
keras.layers.Dense(4, kernel_initializer="he_uniform"),
keras.layers.BatchNormalization(),
keras.layers.ReLU(),
keras.layers.Dense(2, kernel_initializer="he_uniform")
])
qnet.compile(keras.optimizers.Adam(1e-3), "mse")
agent = DQN(qnet, 2, Experience(max_length=10_000), discount_factor_gamma=0.99,
epsilon=1.0, epsilon_decay=0.99999, epsilon_min=0.3, use_target_network=True,
state_preprocessor=None)
rollout = Rolling(agent, env, RolloutConfig(skipframes=2))
test_rollout = Trajectory(agent, test_env)
rewards = deque(maxlen=100)
losses = deque(maxlen=100)
episode = 0
while 1:
episode += 1
def test_experience_constructor_creates_empty_object(self):
xp = Experience()
self.assertIsNone(xp.memoirs)
self.assertEqual(xp.N, 0)
def test_remember_considers_max_size(self):
xp = Experience(max_length=100)
xp.remember(np.arange(120))
self.assertTrue(xp.N, 100)
self.assertListEqual(xp.memoirs[0].tolist(), list(range(20, 120)))
def test_experience_remembers_array(self):
xp = Experience()
xp.remember(np.arange(100))
self.assertEqual(xp.N, 100)
self.assertListEqual(xp.memoirs[0].tolist(), list(range(100)))
def test_experience_constructor_considers_max_size_argument(self):
xp = Experience(max_length=3)
self.assertEqual(xp.max_length, 3)
def reset(self):
self.initial_state = self.env.reset()
return self.empty
envs = [FakeEnv() for _ in range(10)]
test_env = FakeEnv()
actor = Sequential([ # 200, 160
Flatten(input_shape=test_env.shape),
Dense(200, activation="relu"),
Dense(2, activation="softmax")
])
actor.compile(RMSprop(1e-4, rho=0.99), "categorical_crossentropy")
agent = REINFORCE(actor, 2, Experience(), discount_factor_gamma=0.99,
state_preprocessor=None)
rollout = MultiTrajectory(agent, envs)
test_rollout = Trajectory(agent, test_env)
rewards = deque(maxlen=10)
actor_loss = deque(maxlen=80)
actor_utility = deque(maxlen=80)
actor_entropy = deque(maxlen=80)
critic_loss = deque(maxlen=80)
episode = 0
while 1:
canvas_shape, action_shape = env.neurons_required
actor_input = Input(shape=[64, 64, 3], name="actor_input")
critic_input = Input(shape=[64, 64, 3], name="critic_input")
critic_stream = Flatten()(critic_input)
critic_stream = Dense(64, activation="tanh")(critic_stream)
critic_stream = BatchNormalization()(critic_stream)
critic_stream = Dense(32, activation="tanh")(critic_stream)
critic_stream = BatchNormalization()(critic_stream)
value_estimate = Dense(NUM_MOVES, activation="softmax")(critic_stream)
critic = Model(critic_input, value_estimate, name="Critic")
critic.compile(Adam(5e-4), "mse")
agent = DQN(critic, action_space=MOVES, memory=Experience(max_length=10000), discount_factor_gamma=0.99, epsilon=0.7,
state_preprocessor=lambda state: state / 255. - 0.5)
rollout = Rolling(agent, env, config=RolloutConfig(max_steps=512, skipframes=2))
test_rollout = Trajectory(agent, env, config=RolloutConfig(max_steps=512, skipframes=2))
episode = 0
reward_memory = deque(maxlen=10)
losses = deque(maxlen=10)
while 1:
episode += 1
episode_losses = []
for update in range(32):
rollout.roll(steps=4, verbose=0, push_experience=True)