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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20466 |
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| _version_ | 1866916339207110656 |
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| author | Sahabandu, Dinuka Ramasubramanian, Bhaskar Alexiou, Michail Mertoguno, J. Sukarno Bushnell, Linda Poovendran, Radha |
| author_facet | Sahabandu, Dinuka Ramasubramanian, Bhaskar Alexiou, Michail Mertoguno, J. Sukarno Bushnell, Linda Poovendran, Radha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Method for Fast Autonomy Transfer in Reinforcement Learning Sahabandu, Dinuka Ramasubramanian, Bhaskar Alexiou, Michail Mertoguno, J. Sukarno Bushnell, Linda Poovendran, Radha Machine Learning Artificial Intelligence 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. |
| title | A Method for Fast Autonomy Transfer in Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.20466 |