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a2c-PandaReachDense-v3
Charisse-L/a2c-PandaReachDense-v3
a2c-PandaReachDense-v3 is a reinforcement learning model from Charisse-L. 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.
This is a trained model of an A2C (Advantage Actor-Critic) agent playing PandaReachDense-v3 using the stable-baselines3 library and Panda-Gym.
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- May 9, 2026
- Updated
- May 12, 2026
- SHA
- 51efceca
Model card
From the Hugging Face model README
A2C Agent playing PandaReachDense-v3
This is a trained model of an A2C (Advantage Actor-Critic) agent playing PandaReachDense-v3 using the stable-baselines3 library and Panda-Gym.
Environment Description
The PandaReachDense-v3 environment features a Franka Emika Panda robotic arm that must place its end-effector at a target position (green ball). This is a continuous control task with:
- Observation space: Dictionary containing achieved_goal, desired_goal, and observation (position + velocity)
- Action space: 3-dimensional continuous control (x, y, z displacement)
- Reward: Dense reward based on distance to target
Training Results
| Metric | Value |
|---|---|
| Mean Reward | -0.35 |
| Std Reward | ± 0.12 |
| Evaluation Episodes | 10 |
Hyperparameters
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- May 9, 2026
- Updated
- May 12, 2026