Hierarchical Reinforcement Learning

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Prefrontal Mechanisms Underlying Sequential Tasks

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- by Feng-Kuei Chiang
- •
- 8
  Experimental Psychology, Working Memory, Prefrontal Cortex, Psychobiology

Hierarchical Reinforcement Learning: A Survey

Reinforcement Learning (RL) has been an interesting research area in Machine Learning and AI. Hierarchical Reinforcement Learning (HRL) that decomposes the RL problem into sub-problems where solving each of which will be more powerful than solving the entire problem will be our concern in this paper. A review of the state-of-the-art of HRL has been investigated. Different HRL-based domains have been highlighted. Different problems in such different domains along with some proposed solutions have been addressed. It has been observed that HRL has not yet been surveyed in the current existing research; the reason that motivated us to work on this paper. Concluding remarks are presented. Some ideas have been emerged during the work on this research and have been proposed for pursuing a future research.

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Call for Book Chapters: DEEP LEARNING FOR IMAGE PROCESSING APPLICATIONS

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

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Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification

In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult. To solve the problem, we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision (HADS) which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism. HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning. Due to the fact that not all sub-goal points are reachable, a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm. HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals, while those that are not will be removed based on prior knowledge. Our experiments verified the effect of the algorithm.

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Hierarchical goals contextualize local reward decomposition explanations

One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent's future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent's future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies-a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations.

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Aggregate Reinforcement Learning for Multi-Agent Territory Division: The Hide-and-Seek Game

In many applications in Robotics such as disaster rescuing, mine detection, robotic surveillance and warehouse systems, it is crucial to build multi-agent systems (MAS) in which agents cooperate to complete a sequence of tasks. For better performance in such systems, e.g. minimizing duplicate work, agents need to agree on how to divide and plan that sequence of tasks among themselves. This paper targets the problem of territory division in the children's game of Hide-and-Seek as a test-bed for our proposed approach. The problem is solved in a hierarchical learning scheme using Reinforcement Learning (RL). Based on Q-learning, our learning model is presented in detail; definition of composite states, actions, and reward function to deal with multiple agent learning. In addition, a revised version of the standard updating rule of the Q-learning is proposed to cope with multiple seekers. The model is examined on a set of different maps, on which it converges to the optimal solutions. After the complexity analysis of the algorithm, we enhanced it by using state aggregation (SA) to alleviate the state space explosion. Two levels of aggregation are devised: topological and hiding aggregation. After elaborating how the learning model is modified to handle the aggregation technique, the enhanced model is examined by some experiments. Results indicate promising performance with higher convergence rate and up to 10x space reduction.
Keywords:
multi-agent systems, reinforcement learning, hierarchical learning, Q-learning, state aggregation, Hide-and-Seek

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- by Ikuo Takeuchi and +1
  Walid Gomaa
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- 9
  Computer Science, Reinforcement Learning, Multi-Agent Systems, Hierarchical Reinforcement Learning

An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent Settings

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Robust Central Pattern Generators for Embodied Hierarchical Reinforcement Learning

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- by Matthijs Snel
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- 8
  Dynamical Systems, Reinforcement Learning, Autonomous Robotics, Embodiment

Robust central pattern generators for embodied hierarchical reinforcement learning

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- by Shimon Whiteson
- •
- 8
  Dynamical Systems, Reinforcement Learning, Autonomous Robotics, Embodiment

Hierarchical Reinforcement Learning

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