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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2402.15957 |
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| _version_ | 1866912143858728960 |
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| author | Liang, Anthony Tennenholtz, Guy Hsu, Chih-wei Chow, Yinlam Bıyık, Erdem Boutilier, Craig |
| author_facet | Liang, Anthony Tennenholtz, Guy Hsu, Chih-wei Chow, Yinlam Bıyık, Erdem Boutilier, Craig |
| contents | We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions - parts of the episode where the latent state is fixed - and propose three key modifications to existing meta-RL methods: consistency of latent information within sessions, session masking, and prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuous-control and simulated robot assistive tasks, demonstrating that DynaMITE-RL significantly outperforms state-of-the-art baselines in sample efficiency and inference returns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning Liang, Anthony Tennenholtz, Guy Hsu, Chih-wei Chow, Yinlam Bıyık, Erdem Boutilier, Craig Machine Learning We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions - parts of the episode where the latent state is fixed - and propose three key modifications to existing meta-RL methods: consistency of latent information within sessions, session masking, and prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuous-control and simulated robot assistive tasks, demonstrating that DynaMITE-RL significantly outperforms state-of-the-art baselines in sample efficiency and inference returns. |
| title | DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.15957 |