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fetch-pickandplace-sac-her
hhmm1122/fetch-pickandplace-sac-her
fetch-pickandplace-sac-her is a reinforcement learning model from hhmm1122. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
A Soft Actor-Critic (SAC) agent with Hindsight Experience Replay (HER), trained from scratch to solve the MuJoCo FetchPickAndPlace task: reach a block, grasp it, and place it at a target location from sparse reward.
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.zip24 MB · 100%
At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Jun 16, 2026
- Updated
- Jun 16, 2026
- SHA
- c817f4a3
Model card
From the Hugging Face model README
SAC + HER on FetchPickAndPlace-v4
A Soft Actor-Critic (SAC) agent with Hindsight Experience Replay (HER), trained from scratch to solve the MuJoCo FetchPickAndPlace task: reach a block, grasp it, and place it at a target location from sparse reward.
Results
- Evaluation success rate: 100% (deterministic, 30+ episodes)
- Mean episode reward: ~-9.7 (sparse reward; lower magnitude = faster placement)
- Trained for 1.5M timesteps (~10.5 h on CPU)
Usage
import gymnasium as gym, gymnasium_robotics
from stable_baselines3 import SAC
from stable_baselines3.common.buffers import DictReplayBuffer
from huggingface_hub import hf_hub_download
gym.register_envs(gymnasium_robotics)
path = hf_hub_download("hhmm1122/fetch-pickandplace-sac-her", "best_model.zip")
env = gym.make("FetchPickAndPlace-v4", max_episode_steps=50)
model = SAC.load(path, env=env, custom_objects={
"replay_buffer_class": DictReplayBuffer, "replay_buffer_kwargs": {}, "buffer_size": 1})
Training
- Algorithm: SAC + HER (
n_sampled_goal=4,goal_selection_strategy="future") - Network: MLP [512, 512, 512], batch 512, lr 1e-3, gamma 0.95
- Framework: Stable-Baselines3 2.8.0, Gymnasium-Robotics 1.4.2
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Jun 16, 2026
- Updated
- Jun 16, 2026