Reinforcement Learning Reward Function

A Reinforcement Learning Reward Function is a mathematical function that maps agent actions and environment states to scalar reward values in reinforcement learning systems.

• AKA: RL Reward Signal, Reward Mechanism.
• Context:
  • It can typically guide Agent Behavior toward desired outcomes.
  • It can typically encode Task Objectives through numerical feedbacks.
  • It can typically balance Exploration-Exploitation Tradeoffs via reward structures.
  • It can typically shape Learning Trajectories through incremental signals.
  • It can typically incorporate Domain Constraints into optimization processes.
  • ...
  • It can often include Sparse Reward Signals for challenging tasks.
  • It can often combine Multiple Objectives through weighted combinations.
  • It can often adapt Reward Schedules based on training progress.
  • ...
  • It can range from being a Sparse Reinforcement Learning Reward Function to being a Dense Reinforcement Learning Reward Function, depending on its feedback frequency rate.
  • It can range from being a Simple Reinforcement Learning Reward Function to being a Composite Reinforcement Learning Reward Function, depending on its component complexity level.
  • It can range from being a Static Reinforcement Learning Reward Function to being an Adaptive Reinforcement Learning Reward Function, depending on its temporal modification capability.
  • It can range from being a Deterministic Reinforcement Learning Reward Function to being a Stochastic Reinforcement Learning Reward Function, depending on its randomness incorporation degree.
  • ...
  • It can integrate with Policy Gradient Algorithms for direct optimization.
  • It can connect to Value Function Approximators for long-term planning.
  • It can interface with Reward Shaping Frameworks for learning acceleration.
  • ...
• Example(s):
  • Game Playing Reward Functions, such as:
    • Chess Reward Functions assigning piece values and position advantages.
    • Atari Game Reward Functions based on score increments.
  • Robotics Reward Functions, such as:
    • Navigation Reward Functions penalizing distance to goals.
    • Manipulation Reward Functions rewarding task completion stages.
  • Language Model Reward Functions, such as:
    • RLHF Reward Functions incorporating human preferences.
    • Dialogue Reward Functions balancing coherence and informativeness.
  • ...
• Counter-Example(s):
  • Supervised Loss Functions, which compare to ground truth labels.
  • Unsupervised Objective Functions, which lack external feedback.
  • Random Reward Assignments, which provide no learning signal.
• See: Specialized Reward Function, Reinforcement Learning, Markov Decision Process, Q-Learning, Policy Gradient, Reward Shaping, Multi-Objective RL, Inverse Reinforcement Learning, Reward Engineering.

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