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.
Understanding Inverse Reinforcement Learning
Inverse reinforcement learning is a technique that aims to recover the underlying reward function that an agent is implicitly optimizing, given observations of its behavior. Rather than specifying a reward signal manually—which can be difficult for complex tasks—inverse reinforcement learning infers what an expert values by watching demonstrations. This approach is particularly useful in robotics, where programming explicit reward functions for natural movement is challenging, and in autonomous driving, where human driving behavior encodes complex preferences about safety and comfort. The recovered reward function can then be used to train new agents through standard reinforcement learning, often producing more natural and robust behavior than hand-crafted reward shaping. Inverse reinforcement learning connects to broader themes in agentic AI and is a key technique for alignment research, helping ensure artificial intelligence systems learn goals that truly reflect human intentions.
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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.
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.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.