Inverse Reinforcement Learning contrasts with traditional reinforcement learning by focusing on learning what to achieve (the reward function) rather than how to achieve it (the policy). This approach is powerful for situations where specifying a reward function is challenging but examples of optimal or near-optimal behavior are available. IRL algorithms analyze the behaviors of expert agents within an environment to deduce the rewards that those agents are presumably optimizing for. This inferred reward function can then be used to train other agents to perform the task with similar proficiency. IRL has significant applications in areas such as autonomous driving, where understanding human driving behavior can inform the development of better autonomous driving policies, and in robotics, for learning from demonstration.
Historical overview: Inverse Reinforcement Learning emerged from the field of Reinforcement Learning in the late 1990s, with seminal work by Andrew Ng and Stuart Russell published in 2000. This work introduced formal definitions and algorithms for IRL, sparking interest in exploring how agents can learn from the observed behavior of others.
Key contributors: Andrew Ng and Stuart Russell are pivotal figures in the development of IRL, laying foundational theoretical frameworks and algorithms. Their contributions have opened up new pathways for research in learning from demonstration and have influenced a wide array of applications in AI.