Reinforcement learning is a machine learning paradigm focused on sequential decision-making, in which an autonomous agent learns optimal behavior by interacting with a dynamic environment to maximize cumulative reward signals.
My Reading Lists of Deep Learning and Natural Language Processing
Reinforcement learning theory book about foundations of deep RL algorithms with proofs.
RL Notation and Pseudocode for Udacity's MLND program
Hierarchical Bayesian modeling of RLDM tasks, using R & Python
Reinforcement Learning Cheat Sheet
Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.
Solutions to Sutton and Barto book exercises
303 份 AI/LLM 中文讲义，支持在线阅读、PDF 下载和 LaTeX 源码查看 | Stanford CS336/CS224R/CS25 | Berkeley LLM Agents | Agent 工程实践
Modeling agents with probabilistic programs
Simulation based Soft Continuum Robot Control via Reinforcement Learning
Cheatsheet of Reinforcement Learning (Based on Sutton-Barto Book - 2nd Edition)
Notes and solutions to exercises in Sutton and Barto's Reinforcement Learning textbook
A summary of important concepts and algorithms in RL
I have created a small book summarizing concepts from the Reinforcement Learning part of the ATML 2015 course at UCL (https://www.davidsilver.uk/teaching/)
Deep Reinforcement Learning in Autonomous Driving: the A3C algorithm used to make a car learn to drive in TORCS; Python 3.5, Tensorflow, tensorboard, numpy, gym-torcs, ubuntu, latex
🐲 Stanford CS234 : Reinforcement Learning
a comprehensive and detailed AI study notes covering basic knowledge and various cutting-edge fields
Unified notation for Markov Decision Processes PO(MDP)s
Samson's MIT Master's Degree Thesis: "Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging".
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO