Using Rational Networks in Simple Reinforcement Learning (Atati task so far). This repository is used in the Recurrent Rational Networks publication.
This Repository depends on:
- MushroomRL for managing agents, environments, ... etc
- Rational Activation Functions for the learnable rational activation functions
- Pytorch - For the neural network part
First, please clone this repo and go into it:
git clone https://github.com/ml-research/rational_rl
cd rational_rl
A Dockerfile is provided, to create a docker image, please run:
docker build -t rationalrl . # to create a docker image
docker run -ti --gpus all -v $(pwd):/home/rl_paus rationalrl bash
This last command will instantiate a container from your image and run bash into it.*
*You need to have nvidia-docker installed to run docker containers with GPU and CUDA support (otherwise, please drop --gpu all).
To watch a trained Recurrent Rational agent on Kangaroo, please provide its path:
python3 rendering_atari.py updated_agents/DQN_recrat_Kangaroo_s0_e500.zip
Hereafter are provided some compiled example of DQN Agents (left with Leaky ReLU, center with Rational and right with Recurrent Rationals)
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*Agent is orange -
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you can find more gifs in videos/gifs_files/optim/Asterix
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To train a DQN agent on Space Invaders, with recurrent rational and seed set to 0:
python3 train.py -g SpaceInvaders -alg DQN -af rpau -s 0 -
To make the scores plot of the agent on Asterix and store it:
python3 scores_evolutions_graph.py -g Asterix -s
Creating the following image:
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To get the raw scores on all activation functions and all game:
python3 scores_table.py --all -
To get the bar plot comparing rational agents and original [Leaky ReLU] agent.
python3 bar_plot_human_compare.py -h
To get the trained agents, please contact Quentin Delfosse