Reinforcement Learning: An Introduction
by Richard S. Sutton and Andrew G. BartoA foundational text that covers essential concepts and algorithms in reinforcement learning, perfect for deepening your theoretical understanding.
Deep Reinforcement Learning Hands-On
by Maxim LapanAn engaging guide that combines theory and practical coding examples to implement deep reinforcement learning algorithms.
Markov Decision Processes: Discrete Stochastic Dynamic Programming
by Martin L. PutermanAn authoritative resource on MDPs, crucial for understanding the mathematical underpinnings of decision-making in reinforcement learning.
Algorithms for Reinforcement Learning
by Csaba SzepesváriA concise overview of key algorithms in reinforcement learning, offering insights into their implementation and performance.
Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions
by J. Zico Kolter and Andrew Y. NgExplores the intersection of reinforcement learning and optimization, providing a unique perspective on sequential decision-making.
Playing Atari with Deep Reinforcement Learning
by Volodymyr Mnih et al.The groundbreaking paper that introduced Deep Q-Networks, essential for understanding the evolution of RL with deep learning.
Deep Reinforcement Learning: An Overview
by Yuxi LiA comprehensive overview of deep reinforcement learning techniques, bridging the gap between theory and practice.
Reinforcement Learning: State-of-the-Art
by Marco Wiering and Martijn van OtterloA compilation of expert contributions that cover advanced topics and recent developments in reinforcement learning.