This repo is just me playing around with reinforcement learning, running simulations. Most are based on "Reinforcement Learning - An Introduction" by Sutton and Barto.

It currently has a Monte Carlo black jack simulation from chapter 5, and an N Bandits simulation using different agent algorithms from chapter 2.

To set up the environment, run:

conda env create
source activate reinf

Use a deep reinforcement learning algorithm to learn a tetris playing agent.

Takes advantage of a Kears/Theano/Cuda stack to learn a neural net Q-function.

(In Progress)

python src/tetris/simulation.py

To run the debugging web app, install socat and websocketd, then run:

websocketd --port=8080 ./socket_server.sh

To use SSH tunnel to watch the learning process on a different server:

ssh -L 8082:localhost:8080 <user@server;> -p 24

Uses e-greedy methods to find an optimal action value function and policy.

python src/monte_carlo_blackjack.py

The following was an attempt to recreate fig 5.5 in Sutton and Barto, using a Monte Carlo, "on-policy", e-greedy model. There seem to be a few minor differences I haven't worked out yet.

Simulate the static n-bandits problem with e-greedy, e-greedy softmax, pursuit, and reinforcement comparison agents. All settings are in the settings/*.yml files, and should be self-evident

python src/runner.py simulations/<your_simulation>.yml