scholarly journals Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

2021 ◽  
pp. 1-13
Author(s):  
Yuji Takubo ◽  
Hao Chen ◽  
Koki Ho
Keyword(s):  
Reinforcement Learning ◽  
Hierarchical Reinforcement Learning ◽  
Learning Framework

scholarly journals HRLB⌃2: A Reinforcement Learning Based Framework for Believable Bots

2018 ◽  
Vol 8 (12) ◽  
pp. 2453 ◽  
Author(s):  
Christian Arzate Cruz ◽  
Jorge Ramirez Uresti
Keyword(s):  
Reinforcement Learning ◽  
High Dimensional ◽  
State Action ◽  
Hierarchical Reinforcement Learning ◽  
Learning Framework ◽  
Novel Approach ◽  
The Creation ◽  
Action Spaces ◽  
Human Player

The creation of believable behaviors for Non-Player Characters (NPCs) is key to improve the players’ experience while playing a game. To achieve this objective, we need to design NPCs that appear to be controlled by a human player. In this paper, we propose a hierarchical reinforcement learning framework for believable bots (HRLB⌃2). This novel approach has been designed so it can overcome two main challenges currently faced in the creation of human-like NPCs. The first difficulty is exploring domains with high-dimensional state–action spaces, while satisfying constraints imposed by traits that characterize human-like behavior. The second problem is generating behavior diversity, by also adapting to the opponent’s playing style. We evaluated the effectiveness of our framework in the domain of the 2D fighting game named Street Fighter IV. The results of our tests demonstrate that our bot behaves in a human-like manner.

scholarly journals Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

2020 ◽  
Author(s):  
Guojia Wan ◽  
Shirui Pan ◽  
Chen Gong ◽  
Chuan Zhou ◽  
Gholamreza Haffari
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Graph ◽  
Reasoning Process ◽  
Hierarchical Reinforcement Learning ◽  
Learning Framework ◽  
Proposed Model ◽  
The Hierarchical Structure ◽  
Multiple Meanings ◽  
Knowledge Graphs ◽  
Learning Policies

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which human handle cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks.

A Unified Dialogue Management Strategy for Multi-intent Dialogue Conversations in Multiple Languages

2021 ◽  
Vol 20 (6) ◽  
pp. 1-22
Author(s):  
Tulika Saha ◽  
Dhawal Gupta ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Reinforcement Learning ◽  
Virtual Agents ◽  
Dialogue System ◽  
Dialogue Management ◽  
Hierarchical Reinforcement Learning ◽  
Learning Framework ◽  
User Query ◽  
Dialogue Manager ◽  
Task Oriented ◽  
Multiple Languages

Building Virtual Agents capable of carrying out complex queries of the user involving multiple intents of a domain is quite a challenge, because it demands that the agent manages several subtasks simultaneously. This article presents a universal Deep Reinforcement Learning framework that can synthesize dialogue managers capable of working in a task-oriented dialogue system encompassing various intents pertaining to a domain. The conversation between agent and user is broken down into hierarchies, to segregate subtasks pertinent to different intents. The concept of Hierarchical Reinforcement Learning, particularly options , is used to learn policies in different hierarchies that operates in distinct time steps to fulfill the user query successfully. The dialogue manager comprises top-level intent meta-policy to select among subtasks or options and a low-level controller policy to pick primitive actions to communicate with the user to complete the subtask provided to it by the top-level policy in varying intents of a domain. The proposed dialogue management module has been trained in a way such that it can be reused for any language for which it has been developed with little to no supervision. The developed system has been demonstrated for “Air Travel” and “Restaurant” domain in English and Hindi languages. Empirical results determine the robustness and efficacy of the learned dialogue policy as it outperforms several baselines and a state-of-the-art system.

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