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Found 2 papers, 1 papers with code
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
Finally, by interleaving pseudo samples with real ones of the new task, we continually update the state and behavior generators to model progressively diverse behaviors, and regularize the multi-head critic via behavior cloning to mitigate forgetting.
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning
Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal.
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