- Old experiments on RL (2016)
- Solving OpenAI Gym environments (2017-2018)
- Developing an multi agent Tic Tac Toe environment and solving it with Policy Gradients (May 2017)
- Using RL to automatically adapt the cooling in a Data Center (August 2017)
- Controlling Robots via Reinforcement Learning (November 2017)
- Playing and solving the Chrome Dinosaur Game with Evolution Strategies and PyTorch (January 2018)
- Delivery optimization using Reinforcement Learning (January 2019)
- Rubik's Cube optimization (February 2019)
- Multi-Agents simulations (November 2019)
rlis a simple library to do Reinforcement Learning with Keras, it uses old Keras versions and should be updatedhyperionis a simple multi agent simulation library
- Udemy course on RL
- David Silver course on RL at UCL
- Berkeley course on AI
- Spinning up course by OpenAI
- David Silver's Deep Q Learning course
- Demystyfing Deep Reinforcement Learning
- Siraj Raval's notebook on Deep Q Learning
- Deep Reinforcement Learning: Pong from Pixels Andrej Karpathy's blog article on RL (always a reference)
- Evolution strategies - OpenAI
- How evolution taught us the “genetic algorithm”
- Making a robot learn how to move, part 1 — Evolutionary algorithms
- Optimize a quadratic function with ES - Andrej Karpathy
- Evolution modelling with creatures
- Genetic biwalkers
- Evolving stable strategies
- A3C tutorial tutorial by Arthur Juliani
- A3C tutorial with Keras and OpenAI
- A3C explananations and implementations
- ACKTR & A2C - by OpenAI
- ACKTR & A3C implementation in PyTorch
- Actor Critic model with Keras
- Car Racing solving with A3C and this solution as well
- Proximal Policy Optimization - by OpenAI
- PPO,TRPO tutorials
- ELI5 MCTS
- How AlphaGo works
- Original Paper for AlphaGo by David Silver
- Discrete Sequential Prediction of Continuous Actions for Deep RL
- Emotion in Reinforcement Learning Agents and Robots: A Survey
- Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
- Curiosity-driven Exploration by Self-supervised Prediction
- End-to-end optimization of goal-driven and visually grounded dialogue systems
- Deep reinforcement learning from human preferences - OpenAI
- Programmable Agents - Deepmind
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments - OpenAI
- Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback
- Noisy Networks for Exploration
- Hindsight Experience Replay
- DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
- Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning
- A Distributional Perspective on Reinforcement Learning
- Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
- Asynchronous Methods for Deep Reinforcement Learning
- Value Iteration Networks
- A deep reinforcement learning chatbot - MILA
- The Uncertainty Bellman Equation and Exploration
- Deep Reinforcement Learning that Matters
- Overcoming Exploration in Reinforcement Learning with Demonstrations
- Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
- Rainbow: Combining Improvements in Deep Reinforcement Learning
- Optimizing Long Short-Term Memory Recurrent Neural Networks UsingAnt Colony Optimization to Predict Turbine Engine Vibration
- Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
- Emergent Complexity via Multi-Agent Competition
- A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning