这是通过计算 Clipped Surrogate Objective 函数实现的,其核心是 Policy Loss。下面详细介绍 PPO 中的 Policy Loss: ratio=th.exp(log_prob-rollout_data.old_log_prob)policy_loss_1=advantages*ratiopolicy_loss_2=advantages*th.clamp(ratio,1-clip_range,1+clip_range)policy_loss=-th.min(policy_loss_1,p...
该问题是由conda中的stable_baselines3版本引起的。我的stable_baselines3版本是1.1.0。使用pip安装更高...
Here is a quick example of how to train and run PPO on a cartpole environment:import gym from stable_baselines3 import PPO env = gym.make("CartPole-v1") model = PPO("MlpPolicy", env, verbose=1) model.learn(total_timesteps=10_000) obs = env.reset() for i in range(1000): ...
for me about PPO, p.s.: my stable-baselines3 version is v2.0.0 use_gsde = True, full_std= True, log_std_init = -2, sde_sample_freq = 4 """ Custom rollout_state : actually from imitation.data.rollout.py """ policy.reset_noise(venv.num_envs) obs = venv.reset() while not...
我在使用 SB3-contrib Maskable PPO 操作屏蔽算法时遇到错误。文件 ~ naconda3\lib\site-packages\sb3_contri
from stable_baselines3 import ppo commits 2.8 gigabytes of ram on my system: And when creating a vec environment (SubProcVecEnv), it creates all environments with that same commit size, 2.8 gigabytes. However, not one of the environments ever shows using above 200 megabytes. I've tried inst...
❓ Question HI, I am struggling to get PPO to learn effectively on my environment. The reward earned is not smooth and spikes. This is the reward after 7 million steps. I am using a custom env with these settings: action_space = spaces.Bo...
25 changes: 19 additions & 6 deletions 25 stable_baselines3/ppo/ppo.py Original file line numberDiff line numberDiff line change @@ -168,10 +168,12 @@ def train(self) -> None: if self.clip_range_vf is not None: clip_range_vf = self.clip_range_vf(self._current_progress_remaining...
python ppo_atari.py --gpu 0 --env Atlantis --trials 5 The hyperparameters follow that of the original PPO implementation (without LSTM). ppo_atari.py: importargparseimportjsonimportosimportpathlibimporttimeimportuuidimportgymnasiumasgymfromstable_baselines3importPPOfromstable_baselines3.common.env_util...
🐛 Bug When I try to train my agent with a bigger action space (usually around 1400) I get the following error. I tried the solutions found in DLR-RM/stable-baselines3#1596 and #81 which are overwriting the super().__init__(logits=logits)...