GIFT: A New Training Framework to Stabilize Deep RL Policies
A research paper on arXiv (2604.23312) introduces Global stabilisation via Intrinsic Fine Tuning (GIFT), a training framework that directly optimizes the global stability of existing high-performing deep reinforcement learning policies. Deep RL policies often exhibit chaotic state dynamics and high sensitivity to initial conditions, limiting their real-world application. GIFT uses a custom reward function to increase stability while maintaining comparable task performance, improving suitability for real-world control systems.
Key facts
- Paper arXiv:2604.23312 proposes GIFT framework.
- GIFT stands for Global stabilisation via Intrinsic Fine Tuning.
- Deep RL policies show chaotic dynamics and sensitivity to initial conditions.
- GIFT directly optimizes global stability of existing policies.
- Uses a custom reward function.
- Maintains comparable task performance while increasing stability.
- Aims to improve real-world applicability of deep RL.
- Focuses on complex continuous control environments with nonlinear contact forces.
Entities
Institutions
- arXiv