可视化MuZero模型
Visualizing MuZero Models
摘要：
MuZero是一种基于基于模型的强化学习算法，它使用了价值等价动力学模型，在国际象棋、将棋和围棋比赛中取得了最先进的性能。与预测完整下一状态的标准前向动态模型相比，值等效模型被训练以预测未来值，从而强调表示中的值相关信息。虽然价值等价模型在实证上取得了很大的成功，但还没有研究可视化和调查这些模型实际上学习了什么类型的表示。因此，在本文中，我们可视化MuZero代理的潜在表示。我们发现，观察嵌入和内部状态转换动态之间的动作轨迹可能会出现分歧，这可能会导致规划过程中的不稳定。基于这一认识，我们提出了两种正则化技术来稳定MuZero的性能。此外，我们还提供了MuZero的开源实现以及学习表示的交互式可视化工具，这可能有助于进一步研究价值等价算法。
英文摘要：
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model, achieved state-of-the-art performance in Chess, Shogi and the game of Go. In contrast to standard forward dynamics models that predict a full next state, value equivalent models are trained to predict a future value, thereby emphasizing value relevant information in the representations. While value equivalent models have shown strong empirical success, there is no research yet that visualizes and investigates what types of representations these models actually learn. Therefore, in this paper we visualize the latent representation of MuZero agents. We find that action trajectories may diverge between observation embeddings and internal state transition dynamics, which could lead to instability during planning. Based on this insight, we propose two regularization techniques to stabilize MuZero's performance. Additionally, we provide an open-source implementation of MuZero along with an interactive visualizer of learned representations, which may aid further investigation of value equivalent algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
