Toward Robust Long Range Policy Transfer
This work addresses the challenge of robust long-range policy transfer in reinforcement learning, which is incremental as it builds upon existing hierarchical models to improve transferability.
The paper tackles the problem of limited transferability in hierarchical reinforcement learning by proposing a method that trains a combination function and adapts diverse primitive policies to efficiently produce complex behaviors on new tasks, demonstrating outperformance over recent policy transfer methods and showing a broader transferring range.
Humans can master a new task within a few trials by drawing upon skills acquired through prior experience. To mimic this capability, hierarchical models combining primitive policies learned from prior tasks have been proposed. However, these methods fall short comparing to the human's range of transferability. We propose a method, which leverages the hierarchical structure to train the combination function and adapt the set of diverse primitive polices alternatively, to efficiently produce a range of complex behaviors on challenging new tasks. We also design two regularization terms to improve the diversity and utilization rate of the primitives in the pre-training phase. We demonstrate that our method outperforms other recent policy transfer methods by combining and adapting these reusable primitives in tasks with continuous action space. The experiment results further show that our approach provides a broader transferring range. The ablation study also shows the regularization terms are critical for long range policy transfer. Finally, we show that our method consistently outperforms other methods when the quality of the primitives varies.