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arXiv · 2407.20466

A Method for Fast Autonomy Transfer in Reinforcement Learning

Dinuka Sahabandu·Bhaskar Ramasubramanian·Michail Alexiou·J. Sukarno Mertoguno·Linda Bushnell·Radha Poovendran
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Abstract

This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings without requiring extensive computational resources. Our contributions include development of the Multi-Critic Actor-Critic (MCAC) algorithm, establishing its convergence, and empirical evidence demonstrating its efficacy. Our experimental results show that MCAC significantly outperforms the baseline actor-critic algorithm, achieving up to 22.76x faster autonomy transfer and higher reward accumulation. This advancement underscores the potential of leveraging accumulated knowledge for efficient adaptation in RL applications.

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Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou, J. Sukarno Mertoguno, Linda Bushnell, Radha Poovendran. 2024-07-29. A Method for Fast Autonomy Transfer in Reinforcement Learning. https://arxiv.org/abs/2407.20466

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