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Research & Applications - A 40 Page Research Overview of LLM-based Agents

Exploring the emerging landscape of autonomous agents, specifically LLM-based agents, and their impact across diverse domains such as gaming, governance, crypto, robotics, and more.

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Advancements in computational power and data availability brought reinforcement learning (RL) to the forefront, enabling agents to exhibit adaptive behavior in complex environments. RL agents learn through trial and error, interacting with their surroundings and adjusting actions based on rewards. Techniques like Q-learning and SARSA introduced policy optimization, with deep reinforcement learning integrating neural networks to process high-dimensional data (e.g., images, games). AlphaGo exemplifies this approach, using these methods to defeat human champions in Go.