Multi-State TD Target for Model-Free Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911975021215744 |
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| author | Wang, Wuhao Chen, Zhiyong Zhang, Lepeng |
| author_facet | Wang, Wuhao Chen, Zhiyong Zhang, Lepeng |
| contents | Temporal difference (TD) learning is a fundamental technique in reinforcement learning that updates value estimates for states or state-action pairs using a TD target. This target represents an improved estimate of the true value by incorporating both immediate rewards and the estimated value of subsequent states. Traditionally, TD learning relies on the value of a single subsequent state. We propose an enhanced multi-state TD (MSTD) target that utilizes the estimated values of multiple subsequent states. Building on this new MSTD concept, we develop complete actor-critic algorithms that include management of replay buffers in two modes, and integrate with deep deterministic policy optimization (DDPG) and soft actor-critic (SAC). Experimental results demonstrate that algorithms employing the MSTD target significantly improve learning performance compared to traditional methods.The code is provided on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-State TD Target for Model-Free Reinforcement Learning Wang, Wuhao Chen, Zhiyong Zhang, Lepeng Machine Learning Artificial Intelligence 68T05(Primary) Temporal difference (TD) learning is a fundamental technique in reinforcement learning that updates value estimates for states or state-action pairs using a TD target. This target represents an improved estimate of the true value by incorporating both immediate rewards and the estimated value of subsequent states. Traditionally, TD learning relies on the value of a single subsequent state. We propose an enhanced multi-state TD (MSTD) target that utilizes the estimated values of multiple subsequent states. Building on this new MSTD concept, we develop complete actor-critic algorithms that include management of replay buffers in two modes, and integrate with deep deterministic policy optimization (DDPG) and soft actor-critic (SAC). Experimental results demonstrate that algorithms employing the MSTD target significantly improve learning performance compared to traditional methods.The code is provided on GitHub. |
| title | Multi-State TD Target for Model-Free Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence 68T05(Primary) |
| url | https://arxiv.org/abs/2405.16522 |