Preprint Open access
Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning
Adapting to changes in robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based reinforcement learning (RL), replay collected before a dynamics change can slow adaptation, while removing it unnecessarily reduces available training data and can be es …