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Continual Reinforcement Learning with Multi-Timescale Replay
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In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at timescales that are unknown to the agent. The basic MTR buffer comprises a cascade of sub-buffers that accumulate experiences at different timescales, enabling the agent to improve the trade-off between adaptation to new data and retention of old knowledge. We also combine the MTR framework with invariant risk minimization, with the idea of encouraging the agent to learn a policy that is robust across the various environments it encounters over time. The MTR methods are evaluated in three different continual learning settings on two continuous control tasks and, in many cases, show improvement over the baselines.
Forward citations
Cited by 2 Pith papers
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Uncertainty Prioritized Experience Replay
UPER uses ensemble-based epistemic and aleatoric uncertainty to compute an information gain priority for experience replay, outperforming TD-error prioritization on Atari-57.
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Continual Reinforcement Learning for Digital Twin Synchronization Optimization
A continual reinforcement learning scheduler with multi-timescale replay and a resource-constrained actor-critic reduces digital twin state estimation error by up to 55.2% in simulation.
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