def test_off_policy_trainer():
env = VectorEnv("Pendulum-v0", 2)
algo = DDPG("mlp", env, replay_size=100)
trainer = OffPolicyTrainer(algo, env, ["stdout"], epochs=1, evaluate_episodes=2)
assert trainer.off_policy
trainer.train()
trainer.evaluate()
def test_ddpg(self):
env = gym.make("Pendulum-v0")
algo = DDPG("mlp", env, noise=NormalActionNoise, layers=[1, 1])
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, evaluate_episodes=2
)
trainer.train()
trainer.evaluate()
shutil.rmtree("./logs")
def test_td3(self):
env = gym.make("Pendulum-v0")
algo = TD3("mlp", env, noise=OrnsteinUhlenbeckActionNoise, layers=[1, 1])
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, evaluate_episodes=2
)
trainer.train()
trainer.evaluate()
shutil.rmtree("./logs")
def test_dqn(self):
env = gym.make("CartPole-v0")
# DQN
algo = DQN("mlp", env)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, evaluate_episodes=2
)
trainer.train()
trainer.evaluate()
shutil.rmtree("./logs")
# Double DQN with prioritized replay buffer
algo1 = DQN("mlp", env, double_dqn=True, prioritized_replay=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, render=False
)
trainer.train()
shutil.rmtree("./logs")
# Noisy DQN
algo2 = DQN("mlp", env, noisy_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, render=False
)
trainer.train()
shutil.rmtree("./logs")
# Dueling DQN
algo3 = DQN("mlp", env, dueling_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, render=False
)
trainer.train()
shutil.rmtree("./logs")
# Categorical DQN
algo4 = DQN("mlp", env, categorical_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, render=False
)
trainer.train()
shutil.rmtree("./logs")
