SAC全称Soft Actor-Critic,它整合了entropy regularization的思想。论文有以上两篇,第一篇采用模型包括一个actor网络,两个状态价值V网络,两个动作价值Q网络,第二篇的模型包括一个actor网络,四个动作价值Q网络。 model-free深度强化学习算法面临两个主要挑战:高采样复杂度和脆弱的收敛性,因此严重依赖调参,这两个挑战限...
通常在挂钟时间(wall-clock time)和样本效率之间会作一个折中,查看example in PR #439 importgymfromstable_baselines3importSACfromstable_baselines3.common.env_utilimportmake_vec_envenv=make_vec_env("Pendulum-v0",n_envs=4,seed=0)# We collect 4 transitions per call to `ènv.step()`# and perfo...
model_sac = agent.get_model("sac",model_kwargs = SAC_PARAMS) if if_using_sac: # set up logger tmp_path = RESULTS_DIR + '/sac' new_logger_sac = configure(tmp_path, ["stdout", "csv", "tensorboard"]) # Set new logger model_sac.set_logger(new_logger_sac) trained_sac = agent....
from stable_baselines3 import SAC # Custom actor architecture with two layers of 64 units each # Custom critic architecture with two layers of 400 and 300 units policy_kwargs = dict(net_arch=dict(pi=[64, 64], qf=[400, 300])) # Create the agent model = SAC("MlpPolicy", "Pendulum-...
This repository contains an implementation of stable bipedal locomotion control for humanoid robots using the Soft Actor-Critic (SAC) algorithm, simulated within the MuJoCo physics engine and trained using Gymnasium and Stable Baselines 3. robotrlsacgymnasiumhumanoidmujocostablebaselines3 ...
Hello, First of all, thanks for working on this awesome project! I've tried to use the SAC implementation and noticed that it works much slower than TF1 version from stable-baselines. Here is the code for the minimal stable-baselines3 ex...
市场环境的构建遵循了OpenAI Gym的风格，通过数据分割、设置空间维度以及建立市场仿真环境，为深度强化学习智能体提供了实践的舞台。在训练智能体时，Stable Baselines3提供了多种强化学习算法供用户选择，如A2C、DDPG、PPO、TD3和SAC，用户可根据需求选择合适的算法进行训练。交易策略的评估主要通过回测过程来...
# This happens when using SAC/TQC. # SAC has an entropy coefficient which can be fixed or optimized. # If it is optimized, an additional PyTorch variable `log_ent_coef` is defined, # otherwise it is initialized to `None`. if pytorch_variables[name] is None: continue # Set the data ...
572 次提交 提交 .github Add timeout handling for on-policy algorithms (#658) 3年前 docs Add DriverGym project to SB3 project documentation (#665) 3年前 scripts Update doc: SB3-Contrib (#267) 4年前 stable_baselines3 Add timeout handling for on-policy algorithms (#658) ...
SAC TD3 Actions gym.spaces:Box: A N-dimensional box that containes every point in the action space. Discrete: A list of possible actions, where each timestep only one of the actions can be used. MultiDiscrete: A list of possible actions, where each timestep only one action of each ...