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navground_learning 0.1.0 documentation

Contents:

  • Introduction
  • Installation
  • Tutorials
    • Basics
      • Gymnasium Environment
      • Navground-PettingZoo integration
      • Using a ML policy in Navground
    • Empty environment
    • Corridor with obstacle
      • Scenario
      • Learning
    • Crossing
      • Training one agent among many agents
      • Performance of policies trained in single-agent environment
      • Training agents among peers
      • Performance of policies trained in multi-agent environment
    • Periodic Crossing
      • Uniform speeds
      • Different speeds
  • Guides
    • How to extend
  • Reference
    • Types
    • Indices
    • Configuration
    • Rewards functions
    • Single-agent Gymnasium Environment
    • Multi-agent Pettingzoo Environment
    • Policies
    • Imitation Learning
    • Evaluation
    • Saving and Loading
    • Onnx
    • Navground Components
    • Examples
  • .rst

Welcome to navground_learning’s documentation!

Contents

  • Welcome to navground_learning’s documentation!
  • Indices and tables

Welcome to navground_learning’s documentation!#

Contents:

  • Introduction
    • Navground-Gymnasium integration
    • Train ML policies in navground
    • Evaluation
    • Acknowledgement and disclaimer
  • Installation
  • Tutorials
    • Basics
    • Empty environment
    • Corridor with obstacle
    • Crossing
    • Periodic Crossing
  • Guides
    • How to extend
  • Reference
    • Types
    • Indices
    • Configuration
    • Rewards functions
    • Single-agent Gymnasium Environment
    • Multi-agent Pettingzoo Environment
    • Policies
    • Imitation Learning
    • Evaluation
    • Saving and Loading
    • Onnx
    • Navground Components
    • Examples

Indices and tables#

  • Index

  • Module Index

  • Search Page

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Introduction

Contents
  • Welcome to navground_learning’s documentation!
  • Indices and tables

By Jerome Guzzi et al. (IDSIA, USI-SUPSI)

© Copyright 2024, Jerome Guzzi et al. (IDSIA, USI-SUPSI).