A Method for Fast Autonomy Transfer in Reinforcement Learning
- URL: http://arxiv.org/abs/2407.20466v1
- Date: Mon, 29 Jul 2024 23:48:07 GMT
- Title: A Method for Fast Autonomy Transfer in Reinforcement Learning
- Authors: Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou, J. Sukarno Mertoguno, Linda Bushnell, Radha Poovendran,
- Abstract summary: This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer.
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.
- Score: 3.8049020806504967
- License: http://creativecommons.org/licenses/by/4.0/
- 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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