Chunk-wise Model-based Policy Optimization
Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-free planning/imagined rollout training or dynamics gradient-based policy updates. While these approaches achieve comparatively fast learning, they all suffer, to varying degrees, from issues of recurrent compounding errors, high inference-time latency, and instability in backward propagation through time (BPTT). In this work, we propose a model-based RL approach that uses the chunk world model as a differentiable training-time evaluator to optimize the chunk actor without recurrent model invocation. Through the collaborative chunk-wise predict...
| Authors | Weixi Song, Donglin Wang, Yong-Lu Li |
|---|---|
| Primary category | cs.RO |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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