# selectEpsilonGreedyAction: Performs \varepsilon-greedy action selection In ReinforcementLearning: Model-Free Reinforcement Learning

## Description

Implements \varepsilon-greedy action selection. In this strategy, the agent explores the environment by selecting an action at random with probability \varepsilon. Alternatively, the agent exploits its current knowledge by choosing the optimal action with probability 1-\varepsilon.

## Usage

 1 selectEpsilonGreedyAction(Q, state, epsilon) 

## Arguments

 Q State-action table of type hash. state The current state. epsilon Exploration rate between 0 and 1.

## Value

Character value defining the next action.

## References

Sutton and Barto (1998). "Reinforcement Learning: An Introduction", MIT Press, Cambridge, MA.

ReinforcementLearning documentation built on March 26, 2020, 7:38 p.m.