A Method for Fast Autonomy Transfer in Reinforcement Learning
<|reference_start|>A Method for Fast Autonomy Transfer in Reinforcement Learning: 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.<|reference_end|>
@article{sahabandu2024a,
    title={A Method for Fast Autonomy Transfer in Reinforcement Learning},
    author={Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou, J. Sukarno
  Mertoguno, Linda Bushnell, Radha Poovendran},
    journal={arXiv preprint arXiv:2407.20466},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20466},
    primaryClass={cs.LG cs.AI}
}