1. A Method for Fast Autonomy Transfer in Reinforcement Learning
- Author
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Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Alexiou, Michail, Mertoguno, J. Sukarno, Bushnell, Linda, and Poovendran, Radha
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - 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.
- Published
- 2024