Domain Adaptation Using Reinforcement Learning

On the other hand.

Domain adaptation using reinforcement learning. We present a method for using similar techniques in the domain of reinforcement learning allowing an agent to learn domain independent representations for a group of similar games that are visually distinct. Of the 18th international conference on autonomous agents and multiagent systems aamas 2019 montreal canada may 13 17 2019 ifaamas 3 pages. Learning to transfer examples for partial domain adaptation. Domain adversarial reinforcement learning for partial domain adaptation arxiv 10 may 2019 conference.

To address this issue we. In this more general and practical scenario a major challenge is how to select source instances in the shared classes across different domains for positive transfer. Capital letters tend to denote sets of things and lower case letters denote a specific instance of that thing. Data valuation has multiple important use cases.

This issue magnifies when facing continuous domains where the curse of dimensionality is inevitable and generalization is mostly desired. Although reinforcement learning is known as an effective machine learning technique it might perform poorly in complex problems especially real world problems leading to a slow rate of convergence. Domain adaptation for reinforcement learning on the atari. 1 building insights about the learning task 2 domain adaptation 3 corrupted sample discovery and 4 robust learning.

Using gaussian process based reinforcement learning it has been shown that it is possible to construct generic policies which provide acceptable in domain user performance and better performance than can be obtained using under trained domain specific policies. Unsupervised domain adaptation using deep networks with cross grafted stacks arxiv 17 feb 2019. To adaptively learn data values jointly with the target task predictor model we propose a meta learning framework which we name data valuation using reinforcement learning dvrl. Domain adaptation is a well known technique associated with transfer learning which seeks the same goal in machine learning problems especially pattern recognition.

Partial domain adaptation aims to transfer knowledge from a label rich source domain to a label scarce target domain which relaxes the fully shared label space assumption across different domains. The goal of a domain adaptation approach is to learn and find trans formations which can map both source and target domains into a common feature space.