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arXiv CS.AI·Jun 12·5 min read
WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
TL;DR
Reinforcement learning (RL) in robotics faces challenges due to the high cost of data collection, necessitating effective experience transfer. WOMBET, or World Model-Based Experience Transfer, was developed to generate and utilize prior data through uncertainty-penalized planning and adaptive sampling.
✦ Why It Matters
Engineers can leverage WOMBET to enhance data efficiency and performance in RL applications, particularly in robotics.
Key Takeaways
- Implement WOMBET for improved data efficiency in RL tasks.
- Utilize uncertainty-penalized planning to enhance data generation.
- Filter trajectories to focus on high-return, low-uncertainty data.
- Combine offline and online data for effective task adaptation.
Full Summary
Reinforcement learning (RL) is crucial for robotics but often limited by the expensive and risky nature of data collection. WOMBET, or World Model-Based Experience Transfer, addresses this by creating a world model from a source task, which generates offline data through a method that penalizes uncertainty during planning.
It filters trajectories based on high returns and low uncertainty, then fine-tunes the model in a target task using a blend of offline and online data. This dual approach allows for a smooth transition from using prior data to adapting to specific tasks.
Empirical results show that WOMBET improves sample efficiency and final performance on continuous control benchmarks, outperforming strong baseline methods. The findings suggest that jointly optimizing data generation and transfer can lead to more robust RL applications in robotics.