Description Usage Arguments Value References

View source: R/actionSelection.R

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*.

1 | ```
selectEpsilonGreedyAction(Q, state, epsilon)
``` |

`Q` |
State-action table of type |

`state` |
The current state. |

`epsilon` |
Exploration rate between 0 and 1. |

Character value defining the next action.

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

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