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.
@article{arxiv.2407.20466,
title = {A Method for Fast Autonomy Transfer in Reinforcement Learning},
author = {Dinuka Sahabandu and Bhaskar Ramasubramanian and Michail Alexiou and J. Sukarno Mertoguno and Linda Bushnell and Radha Poovendran},
journal= {arXiv preprint arXiv:2407.20466},
year = {2024}
}