def test_sac_shared():
env = VectorEnv("Pendulum-v0", 2)
algo = SAC(
"mlp",
env,
batch_size=5,
shared_layers=[1, 1],
policy_layers=[1, 1],
value_layers=[1, 1],
max_timesteps=100,
)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=5,
max_ep_len=500,
warmup_steps=10,
start_update=10,
max_timesteps=100,
)
trainer.train()
shutil.rmtree("./logs")
def test_atari_env(self):
"""
Tests working of Atari Wrappers and the AtariEnv function
"""
env = VectorEnv("Pong-v0", env_type="atari")
algo = DQN("cnn", env, replay_size=100)
trainer = OffPolicyTrainer(algo, env, epochs=1, max_timesteps=50)
trainer.train()
shutil.rmtree("./logs")
def test_off_policy_trainer():
env = VectorEnv("Pendulum-v0", 2)
algo = DDPG("mlp", env, replay_size=100)
trainer = OffPolicyTrainer(
algo,
env,
["stdout"],
epochs=2,
evaluate_episodes=2,
max_ep_len=300,
max_timesteps=300,
)
assert trainer.off_policy
trainer.train()
trainer.evaluate()
def test_double_dqn(self):
env = VectorEnv("CartPole-v0")
algo = DoubleDQN("mlp", env, batch_size=5, replay_size=100)
assert isinstance(algo.model, MlpValue)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
max_ep_len=200,
epochs=4,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")
def test_prioritized_dqn(self):
env = VectorEnv("Pong-v0", env_type="atari")
algo = PrioritizedReplayDQN("cnn", env, batch_size=5, replay_size=100)
assert isinstance(algo.model, CnnValue)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
max_ep_len=200,
epochs=4,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")
def test_noisy_dqn(self):
env = VectorEnv("CartPole-v0")
algo = NoisyDQN("mlp", env, batch_size=5, replay_size=100, value_layers=[1, 1])
assert algo.dqn_type == "noisy"
assert algo.noisy
assert isinstance(algo.model, MlpNoisyValue)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
max_ep_len=200,
epochs=4,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")
def test_atari_env(self):
"""
Tests working of Atari Wrappers and the AtariEnv function
"""
env = VectorEnv("Pong-v0", env_type="atari")
algo = DQN("cnn",
env,
batch_size=5,
replay_size=100,
value_layers=[1, 1])
trainer = OffPolicyTrainer(algo,
env,
epochs=5,
max_ep_len=200,
warmup_steps=10,
start_update=10)
trainer.train()
shutil.rmtree("./logs")
def test_dueling_dqn(self):
env = VectorEnv("Pong-v0", env_type="atari")
algo = DuelingDQN(
"cnn", env, batch_size=5, replay_size=100, value_layers=[1, 1]
)
assert algo.dqn_type == "dueling"
assert isinstance(algo.model, CnnDuelingValue)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
max_ep_len=200,
epochs=4,
warmup_steps=10,
start_update=10,
max_timesteps=100,
)
trainer.train()
shutil.rmtree("./logs")
def test_prioritized_dqn(self):
env = VectorEnv("CartPole-v0")
algo = PrioritizedReplayDQN("mlp",
env,
batch_size=5,
replay_size=100,
value_layers=[1, 1])
assert isinstance(algo.model, MlpValue)
assert isinstance(algo.replay_buffer, PrioritizedBuffer)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
max_ep_len=200,
epochs=4,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")
def test_td3():
env = VectorEnv("Pendulum-v0", 2)
algo = TD3(
"mlp",
env,
batch_size=5,
noise=OrnsteinUhlenbeckActionNoise,
policy_layers=[1, 1],
value_layers=[1, 1],
)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=5,
max_ep_len=500,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")
def test_ddpg():
env = VectorEnv("Pendulum-v0", 2)
algo = DDPG(
"mlp",
env,
batch_size=5,
noise=NormalActionNoise,
policy_layers=[1, 1],
value_layers=[1, 1],
)
trainer = OffPolicyTrainer(
algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=4,
max_ep_len=200,
warmup_steps=10,
start_update=10,
)
trainer.train()
shutil.rmtree("./logs")