Learning to Reason in Reinforcement Learning

深度强化学习（RL）使用深度神经网络来表示和学习复杂环境中智能代理的最佳决策策略。然而，大多数强化学习方法需要数百万个事件才能收敛到好的策略，这使得强化学习很难应用于需要大量资源的现实场景。该项目旨在为RL配备反事实推理和结果预测等功能，以显着减少所需的交互次数，提高泛化能力，并为代理提供考虑因果关系的能力。这些改进将缩小人工智能和人类能力之间的差距，并扩大RL在现实世界应用中的采用。

Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to significantly reduce the number of interactions required, improve generalisation, and provide the agent with the capability to consider the cause-effects. These improvements would narrow the gap between AI and human capabilities and broaden the adoption of RL in real-world applications.