TQC FetchPickAndPlace-v1 Model
| Property | Value |
|---|---|
| Framework | stable-baselines3 |
| Environment | FetchPickAndPlace-v1 |
| Mean Reward | -8.50 ±3.47 |
| Training Steps | 1,000,000 |
What is tqc-FetchPickAndPlace-v1?
This is a sophisticated reinforcement learning model implementing the Truncated Quantile Critics (TQC) algorithm for robotic manipulation tasks. The model is specifically trained on the FetchPickAndPlace-v1 environment, which involves teaching a robotic arm to pick and place objects. Developed using the stable-baselines3 framework, it achieves a mean reward of -8.50 with a standard deviation of 3.47.
Implementation Details
The model utilizes a MultiInputPolicy architecture with three hidden layers of 512 units each. It employs a HerReplayBuffer for experience replay with online sampling and future goal selection strategy. The implementation includes key hyperparameters such as a learning rate of 0.001, gamma value of 0.98, and a buffer size of 1,000,000.
- Neural Network: 3-layer architecture (512x512x512)
- Dual critic system with n_critics=2
- Tau value of 0.005 for soft updates
- Batch size of 512 samples
Core Capabilities
- Efficient robotic manipulation learning
- Goal-oriented training with HER (Hindsight Experience Replay)
- Stable performance in complex pick-and-place tasks
- Integration with TimeFeatureWrapper for temporal awareness
Frequently Asked Questions
Q: What makes this model unique?
This model combines TQC algorithm with HER replay buffer, making it particularly effective for sparse reward robotic tasks. The implementation includes carefully tuned hyperparameters and a sophisticated neural network architecture optimized for the FetchPickAndPlace environment.
Q: What are the recommended use cases?
This model is ideal for robotic manipulation tasks, particularly in scenarios involving pick-and-place operations. It's well-suited for research and development in robotic control systems where precise object manipulation is required.
