metadata
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- a2c
- robotics
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: '-0.35 +/- 0.12'
name: mean_reward
verified: false
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 |