用乐观贝叶斯采样提升强化学习探索效率，尤其适合稀疏奖励场景。
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
- 基于贝叶斯采样构建联合状态-动作不确定性模型，实现更精准的乐观探索。
- 在MuJoCo和VMAS任务中，学习速度比传统方法快2~3倍，显著加速收敛。
- 适用于机器人控制等复杂连续决策问题，尤其适合奖励稀疏或动作惩罚强的环境。
通过与环境交互学习复杂机器人行为需要合理的探索策略。有效的探索应优先关注可能带来高回报的状态-动作区域，其中乐观探索因其与该目标一致而展现出潜力，有助于实现样本高效的强化学习。然而，现有方法忽视了一个关键点：乐观性应基于连接奖励与状态的信念。为此，我们提出一种实用且理论严谨的乐观探索方法，基于汤普森采样。我们的模型结构是首个能对转移和奖励联合不确定性进行推理的方法。我们在一系列MuJoCo和VMAS连续控制任务上应用该方法。实验表明，在稀疏奖励、动作惩罚及难探索区域环境中，乐观探索能显著加速学习。此外，我们揭示了乐观探索适用的条件，并强调模型不确定性在引导探索中的核心作用。
原文摘要 · Abstract (English)
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state-action space that maximize rewards, with optimistic exploration emerging as a promising direction aligned with this idea and enabling sample-efficient reinforcement learning. However, existing methods overlook a crucial aspect: the need for optimism to be informed by a belief connecting the reward and state. To address this, we propose a practical, theoretically grounded approach to optimistic exploration based on Thompson sampling. Our model structure is the first that allows for reasoning about joint uncertainty over transitions and rewards. We apply our method on a set of MuJoCo and VMAS continuous control tasks. Our experiments demonstrate that optimistic exploration significantly accelerates learning in environments with sparse rewards, action penalties, and difficult-to-explore regions. Furthermore, we provide insights into when optimism is beneficial and emphasize the critical role of model uncertainty in guiding exploration.
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