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Agent Lightning is an open-source framework developed by Microsoft Research for optimizing and training AI agents using reinforcement learning (RL) and other techniques, supporting integration with any agent framework with minimal code changes.

This paper introduces ReAct, an approach that integrates reasoning and acting in large language models (LLMs). ReAct enables LLMs to generate both reasoning traces and task-specific actions in an interleaved manner. This synergy allows reasoning to help induce, track, and update action plans, while actions interface with external sources like knowledge bases to gather more information, overcoming issues of hallucination and error propagation in prior methods.

The paper by DeepMind introduced Deep Q-Networks (DQN), the first deep learning model to learn control policies directly from raw pixel input using reinforcement learning. By combining Q-learning with convolutional neural networks and experience replay, DQN achieved superhuman performance on several Atari 2600 games without handcrafted features or game-specific tweaks. Its impact was profound: it proved deep learning could master complex tasks with sparse, delayed rewards, catalyzing the modern wave of deep reinforcement learning research and paving the way for later breakthroughs like AlphaGo.

The paper introduced AlphaGo, the first program to defeat a human professional Go player without handicap. It combined deep neural networks — trained with supervised learning and reinforcement learning — with Monte Carlo tree search (MCTS), enabling efficient move selection and board evaluation in Go’s massive search space. AlphaGo’s victory against European champion Fan Hui marked a historic AI milestone, showcasing that combining learning-based policies with search can surpass prior handcrafted methods, reshaping both game AI and broader AI research directions.