def test_noisy_dqn(self):
env = VectorEnv("CartPole-v0")
algo = NoisyDQN("mlp", env, batch_size=5, replay_size=100)
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_noisy_dqn(self):
env = VectorEnv("Pong-v0", env_type="atari")
algo = NoisyDQN("cnn", env, batch_size=5, replay_size=100, value_layers=[1, 1])
assert algo.dqn_type == "noisy"
assert algo.noisy
assert isinstance(algo.model, CnnNoisyValue)
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")