Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Onna-san 904-0495, Okinawa, Japan
Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna-san 904-0495, Okinawa, Japan
Neural Comput. 2024 Aug 19;36(9):1854-1885. doi: 10.1162/neco_a_01690.
In reinforcement learning (RL), artificial agents are trained to maximize numerical rewards by performing tasks. Exploration is essential in RL because agents must discover information before exploiting it. Two rewards encouraging efficient exploration are the entropy of action policy and curiosity for information gain. Entropy is well established in the literature, promoting randomized action selection. Curiosity is defined in a broad variety of ways in literature, promoting discovery of novel experiences. One example, prediction error curiosity, rewards agents for discovering observations they cannot accurately predict. However, such agents may be distracted by unpredictable observational noises known as curiosity traps. Based on the free energy principle (FEP), this letter proposes hidden state curiosity, which rewards agents by the KL divergence between the predictive prior and posterior probabilities of latent variables. We trained six types of agents to navigate mazes: baseline agents without rewards for entropy or curiosity and agents rewarded for entropy and/or either prediction error curiosity or hidden state curiosity. We find that entropy and curiosity result in efficient exploration, especially both employed together. Notably, agents with hidden state curiosity demonstrate resilience against curiosity traps, which hinder agents with prediction error curiosity. This suggests implementing the FEP that may enhance the robustness and generalization of RL models, potentially aligning the learning processes of artificial and biological agents.
在强化学习 (RL) 中，人工智能代理通过执行任务来学习最大化数值奖励。探索在 RL 中至关重要，因为代理必须在利用信息之前发现信息。两种鼓励有效探索的奖励是动作策略的熵和信息增益的好奇心。在文献中，熵被广泛应用于促进随机动作选择。在文献中，好奇心被定义为多种方式，促进对新经验的发现。例如，预测误差好奇心，奖励发现他们无法准确预测的观察结果的代理。然而，此类代理可能会被称为好奇心陷阱的不可预测观察噪声所分散注意力。基于自由能原理 (FEP)，本函件提出了隐藏状态好奇心，通过潜在变量的预测先验概率和后验概率之间的 KL 散度来奖励代理。我们训练了六种类型的代理来在迷宫中导航：没有熵或好奇心奖励的基线代理，以及因熵和/或预测误差好奇心或隐藏状态好奇心而获得奖励的代理。我们发现，熵和好奇心导致了有效的探索，特别是两者一起使用时。值得注意的是，具有隐藏状态好奇心的代理对好奇心陷阱具有弹性，这阻碍了具有预测误差好奇心的代理。这表明实施 FEP 可能会增强 RL 模型的鲁棒性和泛化能力，可能会使人工和生物代理的学习过程一致。
