Experience Sharing Based Memetic Transfer Learning for Multiagent Reinforcement Learning

2021 ◽  
Author(s):  
Tonghao Wang ◽  
Xingguang Peng ◽  
Yaochu Jin ◽  
Demin Xu
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Multiagent Reinforcement Learning

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

Lateral Transfer Learning for Multiagent Reinforcement Learning

2021 ◽  
pp. 1-13
Author(s):  
Haobin Shi ◽  
Jingchen Li ◽  
Jiahui Mao ◽  
Kao-Shing Hwang
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Lateral Transfer ◽  
Multiagent Reinforcement Learning

Opponent portrait for multiagent reinforcement learning in competitive environment

2021 ◽  
Author(s):  
Yuxi Ma ◽  
Meng Shen ◽  
Yuhang Zhao ◽  
Zhao Li ◽  
Xiaoyao Tong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Competitive Environment ◽  
Multiagent Reinforcement Learning

scholarly journals Applying a Deep Q Network for OpenAIs Car Racing Game

2020 ◽  
Author(s):  
Ali Fakhry
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Techniques ◽  
Car Racing ◽  
Custom Made ◽  
Learning Technique ◽  
Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

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