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TabularTDLearning

Julia implementations of temporal difference Reinforcement Learning algorithms like Q-Learning and SARSA

Readme

TabularTDLearning

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This repository provides Julia implementations of the following Temporal-Difference reinforcement learning algorithms:

  • Q-Learning
  • SARSA
  • SARSA lambda

Note that these solvers are tabular, and will only work with MDPs that have discrete state and action spaces.

Installation

This package relies on POMDPs.jl. Using POMDPs.jl (should automatically take care of dependencies)

Pkg.add("POMDPs")
import POMDPs
POMDPs.add("TabularTDLearning")

Example

using TabularTDLearning
using POMDPPolicies
using POMDPModels

mdp = GridWorld()
# use Q-Learning
exppolicy = EpsGreedyPolicy(mdp, 0.01)
solver = QLearningSolver(exppolicy, learning_rate=0.1, n_episodes=5000, max_episode_length=50, eval_every=50, n_eval_traj=100)
policy = solve(solver, mdp)
# Use SARSA
solver = SARSASolver(exppolicy, learning_rate=0.1, n_episodes=5000, max_episode_length=50, eval_every=50, n_eval_traj=100)
policy = solve(solver, mdp)
# Use SARSA lambda
solver = SARSALambdaSolver(exppolicy, learning_rate=0.1, lambda=0.9, n_episodes=5000, max_episode_length=50, eval_every=50, n_eval_traj=100)
policy = solve(solver, mdp)

First Commit

03/06/2017

Last Touched

about 1 month ago

Commits

33 commits

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