Reinforcement Learning Environment

The setting in which an RL agent learns and acts through interaction

Overview

A reinforcement learning environment is the framework within which an AI agent interacts, learns, and makes decisions. It defines the state space, available actions, reward structure, and transition dynamics that govern how the agent's actions affect the environment, forming the foundation for learning optimal behavior through experience.

Core Components

  • State space definition
  • Action space specification
  • Reward function design
  • Transition dynamics
  • Observation space
  • Terminal conditions

Implementation

  • Environment modeling
  • State representation
  • Action validation
  • Reward calculation
  • State transitions
  • Termination logic

Key Applications

  • Game environments
  • Robotics simulation
  • Process control
  • Resource management
  • Navigation systems
  • Trading systems