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_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 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_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")