mlsanityData to 14 Oct 2026

Adaptive Policy Learning to Additional Tasks

arXiv 2305.15193 ↗First posted 24 May 2023cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the convergence rate. This paper provides theoretical analysis which guarantees the convergence rate and sample complexity of O(1/T) and O(1/ε), respectively, where T denotes the number of iterations and ε denotes the accuracy of the resulting stationary policy. Furthermore, several challenging numerical simulations, including cartpole, lunar lander, and robot arm, are provided to show that APG obtains similar performance compared to existing deterministic policy gradient methods while utilizing much less data and converging at a faster rate.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 71 references that are in the map. First posted on arXiv 24 May 2023.

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35 of 71 references.

References · 35

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23 more
  1. 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 345
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  23. 2022Cooperative Tuning of Multi-Agent Optimal Control Systemscited by 2

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