Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
This addresses the problem of exploration without domain knowledge for RL practitioners, though it appears incremental as it builds on curriculum learning methods.
The paper tackles the challenge of uninformed search in reinforcement learning by proposing D2C, a curriculum RL method that uses goal-conditional classifiers and out-of-distribution disagreement to design intrinsic rewards and intermediate goals, resulting in outperformance over prior methods in experiments.
Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards. To tackle these challenges, this work proposes a new approach for curriculum RL called Diversify for Disagreement & Conquer (D2C). Unlike previous curriculum learning methods, D2C requires only a few examples of desired outcomes and works in any environment, regardless of its geometry or the distribution of the desired outcome examples. The proposed method performs diversification of the goal-conditional classifiers to identify similarities between visited and desired outcome states and ensures that the classifiers disagree on states from out-of-distribution, which enables quantifying the unexplored region and designing an arbitrary goal-conditioned intrinsic reward signal in a simple and intuitive way. The proposed method then employs bipartite matching to define a curriculum learning objective that produces a sequence of well-adjusted intermediate goals, which enable the agent to automatically explore and conquer the unexplored region. We present experimental results demonstrating that D2C outperforms prior curriculum RL methods in both quantitative and qualitative aspects, even with the arbitrarily distributed desired outcome examples.