def agent_discrete():
agent = PPO(MultiEnv('CartPole-v1', 4),
normalize_state=False,
normalize_reward=False,
model_factory=MLP.factory(),
curiosity_factory=NoCuriosity.factory(),
reward=GeneralizedRewardEstimation(gamma=0.99, lam=0.95),
advantage=GeneralizedAdvantageEstimation(gamma=0.99, lam=0.95),
learning_rate=5e-3,
clip_range=0.2,
v_clip_range=0.2,
c_entropy=1e-2,
c_value=0.5,
n_mini_batches=4,
n_optimization_epochs=5,
clip_grad_norm=0.5)
agent.to(torch.device('cpu'), torch.float32, np.float32)
return agent
def agent_continuous():
agent = PPO(
MultiEnv('Pendulum-v0', 10),
normalize_state=True,
normalize_reward=True,
model_factory=MLP.factory(),
curiosity_factory=ICM.factory(MlpICMModel.factory(),
policy_weight=1,
reward_scale=0.01,
weight=0.2,
intrinsic_reward_integration=0.01),
# curiosity_factory=NoCuriosity.factory(),
reward=GeneralizedRewardEstimation(gamma=0.95, lam=0.1),
advantage=GeneralizedAdvantageEstimation(gamma=0.95, lam=0.1),
learning_rate=4e-4,
clip_range=0.3,
v_clip_range=0.3,
c_entropy=1e-2,
c_value=0.5,
n_mini_batches=32,
n_optimization_epochs=10,
clip_grad_norm=0.5)
agent.to(torch.device('cpu'), torch.float32, np.float32)
return agent
from agents import PPO
from curiosity import NoCuriosity
from envs import MultiEnv
from models import MLP
from reporters import TensorBoardReporter
from rewards import GeneralizedAdvantageEstimation, GeneralizedRewardEstimation
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
reporter = TensorBoardReporter()
agent = PPO(MultiEnv('CartPole-v1', 4, reporter),
reporter=reporter,
normalize_state=False,
normalize_reward=False,
model_factory=MLP.factory(),
curiosity_factory=NoCuriosity.factory(),
reward=GeneralizedRewardEstimation(gamma=0.99, lam=0.95),
advantage=GeneralizedAdvantageEstimation(gamma=0.99, lam=0.95),
learning_rate=5e-3,
clip_range=0.2,
v_clip_range=0.3,
c_entropy=1e-2,
c_value=0.5,
n_mini_batches=4,
n_optimization_epochs=5,
clip_grad_norm=0.5)
agent.to(device, torch.float32, np.float32)
agent.learn(epochs=200, n_steps=500)
agent.eval(n_steps=500, render=True)
def model():
return MLP.factory().create(
Converter.for_space(Box(0, 1, (4, ), np.float32)),
Converter.for_space(Discrete(2)))
