Books

• Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto: Classic RL book. https://www.andrew.cmu.edu/course/10-703/textbook/BartoSutton.pdf

Courses

• Hugging Face’s Deep RL Course: A free, open-source course that guides learners from beginner to expert in deep reinforcement learning. https://lnkd.in/gd3985YQ
• OpenAI Spinning Up: An educational resource that provides a comprehensive introduction to RL concepts and algorithms. https://lnkd.in/ggi7Z7Xh
• Coursera: RL Specialization - Offered by the University of Alberta, this specialization comprises four courses that delve into the fundamentals and applications of RL. Learners will explore adaptive learning systems and implement complete RL solutions. https://lnkd.in/ghgeAeS8
• Deepmind x UCL RL Course: A series of lectures by a leading expert in the field, covering foundational to advanced RL topics. YouTube playlist: https://lnkd.in/gw8re-er. Github: https://lnkd.in/g7rH6yms
• Stanford Online: CS234 - Reinforcement Learning
  • Stanford’s CS234 course provides a comprehensive introduction to RL, covering core challenges and approaches, including generalization and exploration. The curriculum combines lectures with practical assignments to solidify understanding. https://lnkd.in/gUzUvRns
• Stanford Online: CS224R - Deep Reinforcement Learning
  • Focusing on algorithms that combine deep learning with RL, this course emphasizes practical methods for learning behavior from experience. It’s particularly relevant for applications in robotics and control systems. https://lnkd.in/gmb6npfp
• UC Berkeley’s CS 285: Deep RL. Focusing on algorithms that combine deep learning with RL https://lnkd.in/gGbM8E8u

Research Blogs

• Berkeley Artificial Intelligence Research (BAIR) Blog:
  1. Reinforcement learning is supervised learning on optimized data: https://bair.berkeley.edu/blog/2020/10/13/supervised-rl/
  2. Designing Societally Beneficial Reinforcement Learning Systems: https://bair.berkeley.edu/blog/2022/04/29/reward-reports/
  3. Model-Based Reinforcement Learning: Theory and Practice: https://bair.berkeley.edu/blog/2019/12/12/mbpo/
• Microsoft Research Blog:
  1. Reinforcement Learning at Microsoft: https://www.microsoft.com/en-us/research/blog/research-collection-reinforcement-learning-at-microsoft/
  2. Reinforcement Learning Group: https://www.microsoft.com/en-us/research/theme/reinforcement-learning-group/
• OpenAI’s Official Blog:
  1. Spinning Up in Deep RL: https://spinningup.openai.com/en/latest/
  2. Key Concepts in RL: https://spinningup.openai.com/en/latest/spinningup/rl_intro.html
• DeepMind Blog:
  1. Deep Reinforcement Learning: https://deepmind.com/blog/deep-reinforcement-learning/
  2. Fast reinforcement learning through the composition of behaviours: https://deepmind.com/blog/article/fast-reinforcement-learning-through-the-composition-of-behaviours
• Anthropic:
  1. Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback: https://www.anthropic.com/research/training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback
  2. Sycophancy to Subterfuge: Investigating Reward Tampering in Language Models: https://www.anthropic.com/research/reward-tampering
• Liquid AI:
  1. A New Generation of AI Models from First Principles: Liquid AI discusses developing a new generation of AI foundation models built from first principles, going beyond generative pre-trained transformers (GPTs). https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles
  2. From Liquid Neural Networks to Liquid Foundation Models: This research delves into liquid neural networks, a class of brain-inspired systems that remain adaptable and robust even after training. https://www.liquid.ai/research/liquid-neural-networks-research

Other resources

• Reddit’s extensive list.