Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

• Published in: IEEE transaction on neural networks and learning systems 2019-11, Vol.30 (11), p.3409-3418
• Main Authors: Dilokthanakul, Nat, Kaplanis, Christos, Pawlowski, Nick, Shanahan, Murray
• Format: Article
• Language: English
• Subjects: Algorithms; Auxiliary task; Data acquisition; deep reinforcement learning (DRL); Emotional behavior; Exploration; Games; hierarchical reinforcement learning (HRL); intrinsic motivation; Learning; Learning systems; Machine learning; Motivation; Neural coding; Neural networks; Performance enhancement; Reinforcement; Reinforcement learning; Representations; Task analysis; Training; Trajectory; Visualization
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2019.2891792

One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency problem, which stems both from an inability to fully utilize data acquired and from naive exploration strategies. In order to alleviate these problems, we propose a DRL algorithm that aims to improve data efficiency via both the utilization of unrewarded experiences and the exploration strategy by combining ideas from unsupervised auxiliary tasks, intrinsic motivation, and hierarchical reinforcement learning (HRL). Our method is based on a simple HRL architecture with a metacontroller and a subcontroller. The subcontroller is intrinsically motivated by the metacontroller to learn to control aspects of the environment, with the intention of giving the agent: 1) a neural representation that is generically useful for tasks that involve manipulation of the environment and 2) the ability to explore the environment in a temporally extended manner through the control of the metacontroller. In this way, we reinterpret the notion of pixel- and feature-control auxiliary tasks as reusable skills that can be learned via an intrinsic reward. We evaluate our method on a number of Atari 2600 games. We found that it outperforms the baseline in several environments and significantly improves performance in one of the hardest games-Montezuma's revenge-for which the ability to utilize sparse data is key. We found that the inclusion of intrinsic reward is crucial for the improvement in the performance and that most of the benefit seems to be derived from the representations learned during training.