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a2c-PandaReachJointsDense-v1
liamsi/a2c-PandaReachJointsDense-v1
a2c-PandaReachJointsDense-v1 is a machine learning model from liamsi. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This agent was trained using Advantage Actor-Critic (A2C) from Stable-Baselines3, designed to solve the robotic manipulation task PandaReachJointsDense-v3. Training was logged using Weights & Biases.
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Updated Mar 16, 2025
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At a glance
- Access
- Public
- Created
- Mar 16, 2025
- Updated
- Mar 16, 2025
- SHA
- 555bfb5b
Model card
From the Hugging Face model README
🦾 PandaReachJointsDense-v3 with A2C
This agent was trained using Advantage Actor-Critic (A2C) from Stable-Baselines3, designed to solve the robotic manipulation task PandaReachJointsDense-v3. Training was logged using Weights & Biases.
🌎 Environment
- Gym ID:
PandaReachJointsDense-v3 - Task: Robotic manipulation (reach joints) with dense rewards
🧠 Model Configuration
- Algorithm: Advantage Actor-Critic (A2C)
- Policy Network: Multi-Input Policy
- Learning Rate:
0.0003 - Gamma (Discount factor):
0.99 - Number of steps per update (n_steps):
5 - Entropy Coefficient (ent_coef):
0 - Max Gradient Norm:
0.5 - Total Training Timesteps:
500000
📊 Evaluation Details
Evaluation was performed regularly during training (every 1000 timesteps), and the best-performing models were saved according to the evaluation metrics.
- Evaluation Frequency: Every
1000timesteps - Evaluation Mode: Deterministic policy
- Evaluation Environment Wrapped:
Monitor
📈 Logged Metrics on Weights & Biases
- Policy Loss
- Value Loss
- Episode Reward (dense reward)
- Episode Length
- Entropy (to measure policy exploration)
🔗 Useful Links
Enjoy exploring and using the trained agent! 🦾✨
- Created
- Mar 16, 2025
- Updated
- Mar 16, 2025