/Muesli-cartpole

Simple Muesli RL algorithm implementation (PyTorch)

Primary LanguageJupyter Notebook

Muesli-cartpole

This repository is deprecated. I am working now on https://github.com/Itomigna2/Muesli-lunarlander

Links

Colab demo link : https://colab.research.google.com/drive/19qTIgLvevkc5TA9zNjaS5lILWofGvZPJ?usp=sharing

Muesli paper link : https://arxiv.org/abs/2104.06159

CartPole-v1 env document : https://www.gymlibrary.dev/environments/classic_control/cart_pole/

Implemented

  • MuZero network
  • 5 step unroll
  • L_pg+cmpo
  • L_v
  • L_r
  • L_m (5 step)
  • Stacking 8 observations
  • Mini-batch update
  • Hidden state scaled within [-1,1]
  • Gradient clipping by value [-1,1]
  • Dynamics network gradient scale 1/2
  • Target network(prior parameters) moving average update
  • Categorical representation (value, reward model)
  • Normalized advantage
  • Tensorboard monitoring

Differences from paper

  • self play follow main network inferenced policy (originally follow target network)

Memo

This code(.ipynb) is executable in Google Colab. Requirements.txt is from Colab CPU compute backend.