def test_dqn():
env = VectorEnv("CartPole-v0", 2)
# DQN
algo = DQN("mlp", env)
trainer = OffPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
# Double DQN with prioritized replay buffer
# algo1 = DQN("mlp", env, double_dqn=True, prioritized_replay=True)
# trainer = OffPolicyTrainer(algo1, env, log_mode=["csv"], logdir="./logs", epochs=1)
# trainer.train()
# shutil.rmtree("./logs")
# Noisy DQN
algo2 = DQN("mlp", env, noisy_dqn=True)
trainer = OffPolicyTrainer(algo2,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
# Dueling DDQN
algo3 = DQN("mlp", env, dueling_dqn=True, double_dqn=True)
trainer = OffPolicyTrainer(algo3,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
# Categorical DQN
algo4 = DQN("mlp", env, categorical_dqn=True)
trainer = OffPolicyTrainer(algo4,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
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")
def test_dqn_cnn(self):
env = gym.make("Breakout-v0")
# DQN
algo = DQN("cnn", env)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Double DQN with prioritized replay buffer
algo1 = DQN("cnn", env, double_dqn=True, prioritized_replay=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Noisy DQN
algo2 = DQN("cnn", env, noisy_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Dueling DQN
algo3 = DQN("cnn", env, dueling_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Categorical DQN
algo4 = DQN("cnn", env, categorical_dqn=True)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
def test_dqn_cnn(self):
env = VectorEnv("Pong-v0", n_envs=2, env_type="atari")
# DQN
algo = DQN("cnn", env)
trainer = OffPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Double DQN with prioritized replay buffer
algo1 = DQN("cnn", env, double_dqn=True, prioritized_replay=True)
trainer = OffPolicyTrainer(
algo1, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Noisy DQN
algo2 = DQN("cnn", env, noisy_dqn=True)
trainer = OffPolicyTrainer(
algo2, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Dueling DDQN
algo3 = DQN("cnn", env, dueling_dqn=True, double_dqn=True)
trainer = OffPolicyTrainer(
algo3, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
trainer.train()
shutil.rmtree("./logs")
# Categorical DQN
algo4 = DQN("cnn", env, categorical_dqn=True)
trainer = OffPolicyTrainer(
algo4, env, log_mode=["csv"], logdir="./logs", epochs=1, steps_per_epoch=200
)
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)
trainer = OffPolicyTrainer(algo, env, epochs=1, steps_per_epoch=200)
trainer.train()
shutil.rmtree("./logs")
def test_dqn():
env = VectorEnv("CartPole-v0", 2)
# DQN
algo = DQN("mlp", env)
trainer = OffPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_categorical_dqn_cnn():
env = VectorEnv("Pong-v0", n_envs=2, env_type="atari")
# Categorical DQN
algo = DQN("cnn", env, categorical_dqn=True)
trainer = OffPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1,
steps_per_epoch=200)
trainer.train()
shutil.rmtree("./logs")
def test_double_dqn_cnn():
env = VectorEnv("Pong-v0", n_envs=2, env_type="atari")
# Double DQN with prioritized replay buffer
algo = DQN("cnn", env, double_dqn=True, prioritized_replay=True)
trainer = OffPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1,
steps_per_epoch=200)
trainer.train()
shutil.rmtree("./logs")
argument_parser.add_argument("-b", "--batch-size", type=int, default=64)
argument_parser.add_argument("-l", "--length", type=int, default=None)
argument_parser.add_argument("--enable-cuda", action="store_true")
args = argument_parser.parse_args()
if args.enable_cuda:
if torch.cuda_is_available():
device = "cuda"
else:
device = "cpu"
warnings.warn("cuda is ot available. Defaulting to cpu")
else:
device = "cpu"
env = gym_super_mario_bros.make("SuperMarioBros-v0")
env = JoypadSpace(env, SIMPLE_MOVEMENT)
env = MarioEnv(env)
agent = DQN("cnn", env, replay_size=100000, epsilon_decay=100000)
trainer = AdversariaTrainer(
agent=agent,
env=env,
dataset=args.input_path,
possible_actions=SIMPLE_MOVEMENT,
device=device,
length=args.length,
off_policy=True,
evaluate_episodes=1,
)
trainer.train(epochs=args.epochs, lr=args.lr, batch_size=args.batch_size)
trainer.evaluate(render=True)