Multi-State TD Target for Model-Free Reinforcement Learning
<|reference_start|>Multi-State TD Target for Model-Free Reinforcement Learning: 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.<|reference_end|>
@article{wang2024multi-state,
    title={Multi-State TD Target for Model-Free Reinforcement Learning},
    author={Wuhao Wang, Zhiyong Chen, and Lepeng Zhang},
    journal={arXiv preprint arXiv:2405.16522},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16522},
    primaryClass={cs.LG cs.AI}
}