Reinforcement Learning solution for NetHack environment
Authors:
- Rifumo Mzimba
- Caston Nyabadza
- Sarah Wookey
We attempt to solve the NetHack environment using the two different implementations listed below:
- Actor-Critic
- Deep Q-network
The code is set up to run on colab as a jupyter notebook, to set NLE enviroment go see NetHack
For information on how to install the enviroment locally, go see NLE. For Windows install, go see NLE for the docker install or follow the install instructions below for instructions on how to install on linux. :
cd
wget https://github.com/Kitware/CMake/releases/download/v3.18.4/cmake-3.18.4.tar.gz
tar xzvf cmake-3.18.4.tar.gz
cd cmake-3.18.4/
./configure --prefix=$HOME
make
make install
Now put this in your ~/.bashrc
export PATH=$HOME/bin:$PATH
Now run
source ~/.bashrc
To confirm, run the following, which should tell you the version is 3.18
cmake --version
cd
wget http://ftp.gnu.org/gnu/bison/bison-2.3.tar.gz
tar -xvzf bison-2.3.tar.gz
cd bison-2.3
./configure --prefix=$HOME
make
make install
source ~/.bashrc
which bison
cd
wget https://downloads.sourceforge.net/project/flex/flex-2.6.0.tar.gz
tar xzvf flex-2.6.0.tar.gz
cd flex-2.6.0/
./configure --prefix=$HOME
make
make install
source ~/.bashrc
which flex
conda create -n nle python=3.8
conda activate nle
pip install --no-use-pep517 nle
python
import gym
import nle
env = gym.make("NetHackScore-v0")
env.reset()
env.step(1)
env.render()
Nethack baseline Agent:
https://github.com/facebookresearch/nle/blob/master/nle/agent/agent.py
NetHack Learning Environment:
https://github.com/facebookresearch/nle
NetHack Wiki:
https://nethackwiki.com/
NetHack Learning Environment Research Paper:
https://arxiv.org/pdf/2006.13760.pdf