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_vanilla_dqn(self): env = VectorEnv("CartPole-v0") algo = DQN("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_vanilla_dqn(self): env = VectorEnv("Pong-v0", env_type="atari") algo = DQN("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, 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, 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")