Reward Shaping
Reward shaping is the practice of designing intermediate reward signals to guide reinforcement learning agents toward desired behaviors more efficiently. Good reward shaping accelerates training while avoiding unintended shortcuts.
Understanding Reward Shaping
Reward shaping is a technique in reinforcement learning where the reward signal is augmented with additional intermediate rewards to guide the agent toward desired behavior more efficiently. In many environments, the natural reward signal is sparse—an agent might only receive feedback upon completing a task—making learning extremely slow. By providing supplementary rewards for making progress toward the goal, reward shaping accelerates convergence without changing the optimal policy when designed correctly. For example, in robotic navigation, small rewards for moving closer to the target supplement the final success reward. The technique requires careful design to avoid introducing unintended shortcuts or reward hacking, where agents exploit the shaped rewards in unexpected ways. Reward shaping connects to inverse reinforcement learning, which infers reward functions from demonstrations, and is an important consideration in building safe agentic AI systems aligned with human intentions.
Category
Reinforcement Learning
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Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.