# Model-Free Reinforcement Learning

## Documentation for package ‘ReinforcementLearning’ version 1.0.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

computePolicy | Computes the reinforcement learning policy
epsilonGreedyActionSelection | Performs \varepsilon-greedy action selection
experienceReplay | Performs experience replay
gridworldEnvironment | Defines an environment for a gridworld example
lookupActionSelection | Converts a name into an action selection function
lookupLearningRule | Loads reinforcement learning algorithm
policy | Computes the reinforcement learning policy
randomActionSelection | Performs random action selection
ReinforcementLearning | Performs reinforcement learning
replayExperience | Performs experience replay
rl | Performs reinforcement learning
sampleExperience | Sample state transitions from an environment function
sampleGridSequence | Sample grid sequence
selectEpsilonGreedyAction | Performs \varepsilon-greedy action selection
selectRandomAction | Performs random action selection
state | Creates a state representation for arbitrary objects
tictactoe | Game states of 100,000 randomly sampled Tic-Tac-Toe games.