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_vanilla_dqn(self):
env = VectorEnv("CartPole-v0")
algo = DQN("mlp", env, batch_size=5, replay_size=100, value_layers=[1, 1])
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()
trainer.evaluate()
shutil.rmtree("./logs")
def test_double_dqn(self):
env = VectorEnv("Pong-v0", env_type="atari")
algo = DoubleDQN("cnn",
env,
batch_size=5,
replay_size=100,
value_layers=[1, 1])
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()
trainer.evaluate()
shutil.rmtree("./logs")