State of the Art Reinforcement Learning and Research
Cutting-edge reinforcement learning: DeepMind DiscoRL, DQN, PPO, multi-agent systems, and RL research frontiers 2025–2026.
Key Concepts
- Deep Q-Networks (DQN) and variants (Double DQN, Rainbow)
- Policy gradient methods: PPO, A3C, SAC
- Model-based RL and world models
- Multi-agent RL and game-theoretic approaches
- Offline RL and imitation learning
- RLHF and alignment research
- DiscoRL: auto-discovering state-of-the-art algorithms
📺 20 Curated YouTube Videos
▶ Richard Sutton on RL
Dwarkesh Patel
▶ ML and RL Foundations
freeCodeCamp
▶ RL Research Papers
Two Minute Papers
▶ RL from Scratch
Patrick Loeber
▶ Neural Networks RL
Andrej Karpathy
▶ DeepMind RL Course
freeCodeCamp
▶ DQN Reinforcement Learning Research
Two Minute Papers
▶ PPO Explained
Two Minute Papers
▶ RL with Data Science
freeCodeCamp
▶ Reinforcement Learning Crash Course
freeCodeCamp
▶ AlphaGo Neural Networks
Andrej Karpathy
▶ Multi-Agent RL
Patrick Loeber
▶ RL in Python
Tech With Tim
▶ RL Tutorial
freeCodeCamp
▶ RLHF and NLP
freeCodeCamp
▶ RL Algorithms
Two Minute Papers
▶ Policy Gradients
Two Minute Papers
▶ RL Research 2025
freeCodeCamp
▶ Data Science RL
freeCodeCamp
▶ AI and ML
freeCodeCamp