Inverse reinforcement learning

As explained in Reinforcement Learning from Human Feedback

Inverse reinforcement learning is a technique that infers an agent's reward function from observed behavior or trajectories.

Inverse reinforcement learning (IRL) infers an agent’s reward function from observed behavior or trajectories. Standard reinforcement learning usually starts with a reward function and optimizes behavior; IRL reverses that direction, making it useful when the desired objective is demonstrated rather than explicitly specified. section 10.4 section 5.0

IRL is closely related to reward modeling, but the practical emphasis differs:

• IRL learns a reward function from an agent’s behavior. section 10.4
• RLHF reward models learn a signal from human preference data, often predicting which completion is preferred. section 5.0
• Both approaches supply or approximate the learning signal used by reinforcement learning, while differing in implementation and focus. section 5.0

Figure 5.1 The RM in RLHF plays the role of the environment component that returns rewards in standard RL. The key difference is that in RLHF, we get to control and learn this reward function from human preferences, rather than it being fixed by the environment.

Shows how a learned reward model replaces the fixed environment reward as the learning signal in RLHF, clarifying IRL’s relationship to reward modeling. section 5.0

How Reinforcement Learning from Human Feedback covers it

How deep it goes

Defines reward inference from behavior or trajectories, but does not develop an algorithm, derivation, or implementation.

Examples

Uses games, nuclear-fusion control, rapid robotics, and physical-system energy consumption as reward-driven application contexts.

What it assumes

Assumes reinforcement learning, reward functions, trajectories, and human preferences as familiar concepts.

What you can do after

Evaluate/understand how behavior-based reward inference relates to preference-based reward modeling and RLHF.

How it connects

Connects inverse reinforcement learning to reward modeling, reinforcement learning from human feedback, deep reinforcement learning, and value alignment.

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Related concepts

• reward signaluses · A reward signal is an evaluative feedback value representing the desirability or payoff of an action, outcome, or state.
• softmax decision ruleused by · A softmax decision rule selects actions probabilistically according to a softmax transformation of their scores or utilities.
• reward modelalternative to · A reward model is a learned model that assigns scores to outputs, actions, or outcomes to represent their desirability according to preferences or evaluation criteria.

This explainer was generated by Manning's knowledge graph from the text of Reinforcement Learning from Human Feedback. The section links point to the parts of the book it draws on.