Improving state representation boosts navigation success more than goal encoding
Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?
Machine LearningArtificial Intelligence
Summary
Goal-conditioned reinforcement learning helps computers learn how to reach goals, relying on how goals and current states are shown to the learning system. The authors found that making the goal descriptions better didn’t really improve the computer’s performance in maze navigation tasks. Instead, showing the computer’s current location in a better way had a much bigger effect. They also showed a simple method to improve how the current location is represented, which helped with the hardest navigation challenges. This means that in these tasks, focusing on how the computer understands where it is matters more than how it views the goal.
What this means in practice
- •For robotics engineers: Improve robot navigation in known environments by enhancing the robot’s current location encoding rather than refining goal representations.
- •For video game ai developers: Develop more effective in-game navigation agents by optimizing how agents represent their current positions without altering goal descriptions.
Authors
Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque, Shifat E Arman, Md Mehedi Hasan
Abstract
Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy. While recent methods encode goals via temporal distance, occupancy, or controllability, it remains unclear how much downstream performance actually depends on representation quality. We study this in offline GCRL by constructing an exact temporal-distance goal representation in deterministic mazes. We then systematically corrupt its geometric quality while keeping the downstream learner fixed. Across OGBench navigation tasks and two algorithms, large changes in goal-representation quality produce almost no change in performance. However, applying the same interventions to the agent's current state more than doubles success, revealing the state pathway as the true bottleneck. Building on this insight, we show that simple random Fourier positional encodings substantially improve performance on the hardest navigation tasks without map information or objective modifications. Overall, our findings suggest that in state-based offline navigation, improving how the agent's current state is represented matters far more than refining the goal representation. Code will be released soon.