Value function reveals environment rules learned by agent
Visualizing the World Model in an Agent's Value Function
Artificial IntelligenceMachine Learning
Summary
Sometimes, AI that learns by trial and error might secretly figure out how the world works, even if it wasn’t explicitly taught. The authors studied a particular AI agent playing a game and found that its measure of how good a situation is (called the value function) actually understood things like when it could collect a diamond or how eating affects health. They used special tests to confirm the AI had learned these ideas. They also talk about what this means for safety and the limitations of current AI systems.
What this means in practice
- •For reinforcement learning engineers: Design agents whose value functions provide interpretable insights into environment dynamics and rules to improve debugging and trust.
- •For game ai developers: Use value function analysis to better understand and refine agent behavior rules in simulated game settings.
Tested on simulated data.
Authors
Shawheen Naderi, Fatemeh Akherati, Yubei Chen
Abstract
Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions which hold everything about a state fixed, besides a targeted change, to explore if the value function of a Proximal Policy Optimization agent forms a world model of the Crafter environment. Despite only being trained to optimize reward, we discover that the value function learns concepts such as the rules governing when a diamond is collectible, water and food having an impact on health, heuristics regarding inventory, and more. Finally, we discuss the safety implications of our visualizations and some shortcomings of current reinforcement learning agents that will prevent us from building truly advanced machine intelligence.