pole

Documentation

Overview

The pole balancing experiments is classic Reinforced Learning task proposed by Richard Sutton and Charles Anderson. In this experiment we will try to teach RF model of balancing pole placed on the moving cart.

Index

• type CartDoublePoleGenerationEvaluator
  • func (ex CartDoublePoleGenerationEvaluator) GenerationEvaluate(pop *genetics.Population, epoch *experiments.Generation, ...) (err error)
• type CartPole
• type CartPoleGenerationEvaluator
  • func (ex CartPoleGenerationEvaluator) GenerationEvaluate(pop *genetics.Population, epoch *experiments.Generation, ...) (err error)

Types

type CartDoublePoleGenerationEvaluator

type CartDoublePoleGenerationEvaluator struct {
	// The output path to store execution results
	OutputPath string
	// The flag to indicate whether to apply Markov evaluation variant
	Markov bool

	// The flag to indicate whether to use continuous activation or discrete
	ActionType experiments.ActionType
}

The double pole-balancing experiment both Markov and non-Markov versions

func (CartDoublePoleGenerationEvaluator) GenerationEvaluate

func (ex CartDoublePoleGenerationEvaluator) GenerationEvaluate(pop *genetics.Population, epoch *experiments.Generation, context *neat.NeatContext) (err error)

Perform evaluation of one epoch on double pole balancing

type CartPole

type CartPole struct {
	// contains filtered or unexported fields
}

The structure to describe cart pole emulation

type CartPoleGenerationEvaluator

type CartPoleGenerationEvaluator struct {
	// The output path to store execution results
	OutputPath string
	// The flag to indicate if cart emulator should be started from random position
	RandomStart bool
	// The number of emulation steps to be done balancing pole to win
	WinBalancingSteps int
}

The single pole balancing experiment entry point. This experiment performs evolution on single pole balancing task in order to produce appropriate genome.

func (CartPoleGenerationEvaluator) GenerationEvaluate

func (ex CartPoleGenerationEvaluator) GenerationEvaluate(pop *genetics.Population, epoch *experiments.Generation, context *neat.NeatContext) (err error)

This method evaluates one epoch for given population and prints results into output directory if any.