The ability to reuse previous policies is an important aspect of human intelli- gence. To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide when to reuse and which source policies to reuse.
To speed up this process, we propose to extend the concept to multi-task learning by adapting Policy Reuse, a Transfer Learning approach from classic RL, to ...
Oct 15, 2022 · To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide when to reuse and which source policies to reuse.
Feb 12, 2017 · The approach is termed Deep Reinforcement Learning and combines the classic field of Reinforcement Learning (RL) with the representational power ...
Oct 27, 2023 · Most TRL methods learn, transfer, and reuse black-box policies, which is hard to explain 1) when to reuse, 2) which source policy is effective, ...
Policy Reuse in Deep Reinforcement Learning - ResearchGate
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This paper introduces the Case-based Inference approach to Reinforcement Learning so as to reuse knowledge from previously solved tasks. We propose the Case- ...
In this paper, we propose a novel TRL method called ProgrAm guiDeD poLicy rEuse (PADDLE) that can measure the logic similarities between tasks and transfer ...
May 6, 2024 · To achieve this, we propose a hybrid deci- sion model that synthesizes high-level logic programs and learns low-level DRL policy to learn source ...
We provide empirical results demonstrating that Policy Reuse improves the learning performance over different strategies that learn without reuse. Interestingly ...
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Apr 16, 2022 · In this paper, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL).