Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model It is just the paper proposing MuZero. MuZero is quite famous when I write this note(Jan 2021). Lots of people tried to reproduce the incredible performance of the paper. Some of well-known implementations like muzero-general give a clear and modular implementation of MuZero. If you are interested in MuZero, you can play with it. Well, let’s diving into the paper....
Mastering the game of Go without human knowledge The paper propose AlphaGo Zero which is known as self-playing without human knowledge. Reinforcement learning in AlphaGo Zero $$ (p, v) = f_{\theta} $$ $$ l = (z - v)^2 - \pi^T log(p) + c||\theta||^2 $$ Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm The paper propose AlphaZero which is known as self-playing to compete any kinds of board game....
AlphaGo is quite famous when I was a freshman of college. It somehow is the reason that I was addicted to Reinforcement Learning. Thus Our journey of model-based RL will start here. Although it is not the first one that propose model-based RL, I still believe it will give a big picture of model-based RL. Mastering the game of Go with deep neural networks and tree search Introduction AlphaGo combines 2 kinds of model, including policy network and value network....