This file will become your README and also the index of your documentation.

pip install -r requirements
pip install -e .

• Representation function - $h_{\theta}(o_1, o_2,...o_t)$: It takes the history of observations and produces a hidden state $s_o$ of the observations

• Prediction function - $f_{\theta}(s^k)$: It takes the hidden state of the observation and predicts the policy $\pi^k$ and the value $v^k$

• Dynamic function - $g_{\theta}(s^{k-1}, a^k) = r^k, s^k$: It takes the current state and an action, it predicts the next state and reward

Monte Carlo Tree Search (MCTS: to guide the exploration of the game state space and select the most promising actions

• A replay buffer share between agents
• Monte Carlo Tree Search

Resources that i used to implement MuZero - MuZero - Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model | RL Paper explained

• MCTS
  • Recreating DeepMind's AlphaZero - AI Plays Connect 4 - Part 2: Intro to Monte Carlo Tree Search: https://youtu.be/HikhrP5sgQo