Latent Structure Matching for Knowledge Transfer in Reinforcement Learning

Hierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents

This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...

Knowledge Transfer in Reinforcement Learning

hierarchical functional concepts for knowledge transfer among reinforcement learning agents

this article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for reinforcement learning agents. these definitions are used as a tool of knowledge transfer among agents. the agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. in other words, the agents are assumed t...

Using Options for Knowledge Transfer in Reinforcement Learning

One of the original motivations for the use of temporally extended actions, or options, in reinforcement learning was to enable the transfer of learned value functions or policies to new problems. Many experimenters have used options to speed learning on single problems, but options have not been studied in depth as a tool for transfer. In this paper we introduce a formal model of a learning pr...

Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay

The process for transferring knowledge of multiple reinforcement learning policies into a single multi-task policy via distillation technique is known as policy distillation. When policy distillation is under a deep reinforcement learning setting, due to the giant parameter size and the huge state space for each task domain, it requires extensive computational efforts to train the multi-task po...