Integrating Reinforcement and Episodic Learning in a General Cognitive Architecture

该项目旨在扩展Soar架构，以包括情景学习和强化学习。虽然Soar是一个成熟和广为人知的认知架构，但这些扩展将通过为Soar提供对先前事件的情节记忆来增强Soar，从而改善未来的决策和改善其响应奖励的行为的能力。这项研究将探索这些学习机制之间可能的协同效应，包括使用强化学习来学习记忆编码和回忆策略，使用内部基于情节的模型来促进强化学习，以及使用情绪模型来影响学习速度。鉴于情节记忆是人类认知的一个重要方面，该项目还将加强Soar作为一种认知模型。除了在Soar上正在进行的工作之外，该项目还将建立在基于案例推理等密切相关领域的结果的基础上。

This project aims to extend the Soar architecture to include episodic and reinforcement learning. Although Soar is a mature and widely known cognitive architecture, the extensions will enhance Soar by providing it an episodic memory of prior events that can improve future decision making and the ability to improve its behavior in response to rewards. This research will explore possible synergies between these learning mechanisms including using reinforcement learning for learning memory encoding and recall strategies, using internal episode-based models to boost reinforcement learning and using a model of emotions for affecting learning rates. Given that episodic memory is an important aspect of human cognition, this project will also strengthen Soar as a cognitive model. Besides building on on-going work on Soar, this project will build on results from closely allied fields such as Case-Based Reasoning.