def test_ppo1(self):
env = VectorEnv("CartPole-v0")
algo = PPO1("mlp", env, rollout_size=128)
trainer = OnPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1
)
trainer.train()
shutil.rmtree("./logs")
def test_ppo1_cnn(self):
env = VectorEnv("Pong-v0", env_type="atari")
algo = PPO1("cnn", env, rollout_size=128)
trainer = OnPolicyTrainer(
algo, env, log_mode=["csv"], logdir="./logs", epochs=1
)
trainer.train()
shutil.rmtree("./logs")
def test_vpg():
env = VectorEnv("CartPole-v0")
algo = VPG("mlp", env)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_a2c_shared():
env = VectorEnv("CartPole-v0", 1)
algo = A2C("mlp", env, shared_layers=(32, 32), rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_a2c_discrete(self):
env = VectorEnv("CartPole-v0", 1)
algo = A2C("mlp", env, rollout_size=128)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
trainer.evaluate()
shutil.rmtree("./logs")
def test_on_policy_trainer():
env = VectorEnv("CartPole-v1", 2)
algo = PPO1("mlp", env, rollout_size=128)
trainer = OnPolicyTrainer(algo,
env, ["stdout"],
epochs=2,
evaluate_episodes=2,
max_timesteps=300)
assert not trainer.off_policy
trainer.train()
trainer.evaluate()
def main(args):
ALGOS = {
"sac": SAC,
"a2c": A2C,
"ppo": PPO1,
"ddpg": DDPG,
"td3": TD3,
"vpg": VPG,
"dqn": DQN,
}
algo = ALGOS[args.algo.lower()]
env = VectorEnv(args.env,
n_envs=args.n_envs,
parallel=not args.serial,
env_type=args.env_type)
logger = get_logger(args.log)
trainer = None
if args.algo in ["ppo", "vpg", "a2c"]:
agent = algo(
args.arch, env,
rollout_size=args.rollout_size) # , batch_size=args.batch_size)
trainer = OnPolicyTrainer(
agent,
env,
logger,
epochs=args.epochs,
render=args.render,
log_interval=args.log_interval,
)
else:
agent = algo(args.arch,
env,
replay_size=args.replay_size,
batch_size=args.batch_size)
trainer = OffPolicyTrainer(
agent,
env,
logger,
epochs=args.epochs,
render=args.render,
warmup_steps=args.warmup_steps,
log_interval=args.log_interval,
)
trainer.train()
trainer.evaluate()
env.render()
def test_save_params():
"""
test saving algorithm state dict
"""
env = VectorEnv("CartPole-v0", 1)
algo = PPO1("mlp", env)
trainer = OnPolicyTrainer(algo,
env, ["stdout"],
save_model="test_ckpt",
save_interval=1,
epochs=1)
trainer.train()
assert len(os.listdir("test_ckpt/PPO1_CartPole-v0")) != 0
def test_load_params():
"""
test loading algorithm parameters
"""
env = VectorEnv("CartPole-v0", 1)
algo = PPO1("mlp", env)
trainer = OnPolicyTrainer(
algo,
env,
epochs=0,
load_model="test_ckpt/PPO1_CartPole-v0/0-log-0.pt")
trainer.train()
rmtree("logs")
def test_custom_ppo1(self):
env = VectorEnv("CartPole-v0", 1)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
actorcritic = custom_actorcritic(state_dim, action_dim)
algo = PPO1(actorcritic, env)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_custom_vpg(self):
env = VectorEnv("CartPole-v0", 1)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
policy = custom_policy(state_dim, action_dim)
algo = VPG(policy, env)
trainer = OnPolicyTrainer(algo,
env,
log_mode=["csv"],
logdir="./logs",
epochs=1)
trainer.train()
shutil.rmtree("./logs")
def test_load_params(self):
"""
test loading algorithm parameters
"""
env = VectorEnv("CartPole-v0", 1)
algo = PPO1("mlp", env)
trainer = OnPolicyTrainer(
algo,
env,
epochs=0,
load_hyperparams="test_ckpt/PPO1_CartPole-v0/0-log-0.toml",
load_weights="test_ckpt/PPO1_CartPole-v0/0-log-0.pt",
)
trainer.train()
rmtree("logs")