但是,在on-policy 算法定义自定义policy时或者在policy_kwargs中设置share_features_extractor=False的off-policy 算法时不共享。 importgymimporttorchasthimporttorch.nnasnnfromstable_baselines3importPPOfromstable_baselines3.common.torch_layersimportBaseFeaturesExtractorclassCustomCNN(BaseFeaturesExtractor):""" :par...
# 第一个参数指定网络类型,可选MlpPolicy,CnnPolicy,MultiInputPolicy # 如果想使用自定义的网络结构,可以在 policy_kwargs 参数中进行定义 model = PPO("MlpPolicy", env, verbose=0) # 训练之前随机的 policy,可以获得的平均 reward 比较低 mean_reward, std_reward = evaluate_policy(model, env, n_eval_...
from stable_baselines3 import A2C # 使用A2C算法 from stable_baselines3.common.vec_env import VecFrameStack # 在之前的笔记中,我们一次只训练了一个环境,现在我们一次训练4个环境来加快训练 from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.env_util import make_...
policy_kwargs=dict(features_extractor_class=CnnExtractor,features_extractor_kwargs=dict(features_dim=256),) 来定义。默认情况下它是一个FlattenExtractor,会把输入数据展平。 如果是ActorCriticCNNPolicy,那默认情况下features_extractor是一个NatureCNN,结构如下 self.cnn=nn.Sequential(nn.Conv2d(n_input_channel...
stablebaseline3 Sayyor Y 1,238 asked Jul 7 at 22:11 0 votes 0 answers 23 views RL Model training I trained a PPO algorithm using stablebaselines3, but when loading the model this happens NotImplementedError: <class 'stable_baselines3.common.policies.ActorCriticCnnPolicy'> observation spac...
(env) # prepare the einvironment to use stablebaselines env = ss.concat_vec_envs_v1(env, 2, num_cpus=1, base_class='stable_baselines3') # PPO model = PPO(CnnPolicy, env, verbose=3, gamma=0.95, n_steps=256, ent_coef=0.0905168, learning_rate=0.00062211, vf_coef=0.042202, max_...
classBaseAlgorithm(ABC):""" The base of RL algorithms :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: The environment to learn from (if registered in Gym, can be str. Can be None for loading trained models) :param learning_rate: learning rate for the...
Policy Networks Stable Baselines3 provides policy networks for images (CnnPolicies), other type of input features (MlpPolicies) and multiple different inputs (MultiInputPolicies). Warning For A2C and PPO, continuous actions are clipped during training and testing (to avoid out of bound error). SA...
ただしStable BaselinesではLSTMを使った方策も用意されていますが、Stable Baselines3では MlpPolicy(多層パーセプトロン) か CnnPolicy(CNN) の2種類しかありません。 LSTMなどを使いたい場合は、独自の方策ネットワークを作ることになりますが、以下のリンクにある通り、独自のクラスを作ること...
`CnnPolicy` instead of `MlpPolicy`)\n" "If you are using a custom environment,\n" "please check it using our env checker:\n" "https://stable-baselines3.readthedocs.io/en/master/common/env_checker.html" ) n_input_channels = observation_space.shape[0] self.cn...