Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results

2007 ◽  
pp. 159-195 ◽  
Author(s):  
Sridhar Mahadevan
Keyword(s):  
Reinforcement Learning ◽  
Average Reward ◽  
Empirical Results

Designing Internal Reward of Reinforcement Learning Agents in Multi-Step Dilemma Problem

2013 ◽  
Vol 17 (6) ◽  
pp. 926-931 ◽  
Author(s):  
Yoshihiro Ichikawa ◽  
◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Local Convergence ◽  
Average Reward ◽  
Q Value ◽  
Learning Agents ◽  
Learning Agent ◽  
Simulation Results ◽  
External Reward

This paper proposes the reinforcement learning agent that estimates internal rewards using external rewards in order to avoid conflict in multi-step dilemma problem. Intensive simulation results have revealed that the agent succeeds in avoiding local convergence and obtains a behavior policy for reaching a higher reward by updating the Q-value using the value that is subtracted the average reward from an external reward.

scholarly journals Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation

2019 ◽  
Vol 33 ◽  
pp. 8465-8472 ◽  
Author(s):  
Qiuyuan Huang ◽  
Zhe Gan ◽  
Asli Celikyilmaz ◽  
Dapeng Wu ◽  
Jianfeng Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Semantic Concept ◽  
Sentence Generation ◽  
Visual Storytelling ◽  
Empirical Results ◽  
Low Level ◽  
Story Generation ◽  
End To End ◽  
High Level

We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task. Within our framework, the task of generating a story given a sequence of images is divided across a two-level hierarchical decoder. The high-level decoder constructs a plan by generating a semantic concept (i.e., topic) for each image in sequence. The low-level decoder generates a sentence for each image using a semantic compositional network, which effectively grounds the sentence generation conditioned on the topic. The two decoders are jointly trained end-to-end using reinforcement learning. We evaluate our model on the visual storytelling (VIST) dataset. Empirical results from both automatic and human evaluations demonstrate that the proposed hierarchically structured reinforced training achieves significantly better performance compared to a strong flat deep reinforcement learning baseline.

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