Explainable Reinforcement Learning Agents Using World Models

World Models make RL explanations more intuitive: The paper demonstrates that using World Models—systems that predict how environments change in response to actions—can generate explanations for reinforcement learning agents that are easier for non-experts to understand compared to traditional approaches.

Counterfactual explanations are more effective than showing alternatives alone: Simply showing users what action the agent could have taken isn't sufficient; the paper found that explanations showing "what the world should have been like" for the agent to prefer a different action significantly increased user understanding of why the agent made its actual decision.

Reverse World Models reveal hidden reasoning: By introducing a Reverse World Model that works backward from desired actions to predict necessary world states, the researchers created a way to expose the agent's underlying decision logic—addressing the challenge that non-experts typically cannot modify agents or their policies directly.

Potential for user control through environmental manipulation: The paper suggests these explanations could help users learn to influence agent behavior by manipulating the environment itself, rather than requiring technical knowledge to reprogram the agent—making AI systems more controllable for non-technical stakeholders.

Temporal decision-making requires different explanation approaches: The paper highlights that explaining reinforcement learning is fundamentally more complex than explaining static AI systems because agents make sequential decisions over time, requiring explanation methods specifically designed for this temporal dimension.