Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910367502827520 |
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| author | Ding, Zihan Jin, Chi |
| author_facet | Ding, Zihan Jin, Chi |
| contents | Score-based generative models like the diffusion model have been testified to be effective in modeling multi-modal data from image generation to reinforcement learning (RL). However, the inference process of diffusion model can be slow, which hinders its usage in RL with iterative sampling. We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online. For offline RL, we demonstrate the expressiveness of generative models as policies from multi-modal data. For offline-to-online RL, the consistency policy is shown to be more computational efficient than diffusion policy, with a comparable performance. For online RL, the consistency policy demonstrates significant speedup and even higher average performances than the diffusion policy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16984 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning Ding, Zihan Jin, Chi Machine Learning Score-based generative models like the diffusion model have been testified to be effective in modeling multi-modal data from image generation to reinforcement learning (RL). However, the inference process of diffusion model can be slow, which hinders its usage in RL with iterative sampling. We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online. For offline RL, we demonstrate the expressiveness of generative models as policies from multi-modal data. For offline-to-online RL, the consistency policy is shown to be more computational efficient than diffusion policy, with a comparable performance. For online RL, the consistency policy demonstrates significant speedup and even higher average performances than the diffusion policy. |
| title | Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.16984 |