hierarchical functional concepts for knowledge transfer among reinforcement learning agents

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 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...

Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer

Methods of deep machine learning enable to to reuse low-level representations efficiently for generating more abstract high-level representations. Originally, deep learning has been applied passively (e.g., for classification purposes). Recently, it has been extended to estimate the value of actions for autonomous agents within the framework of reinforcement learning (RL). Explicit models of th...

Hierarchical Reinforcement Learning for Communicating Agents

This paper proposes hierarchical reinforcement learning (RL) methods for communication in multiagent coordination problems modelled as Markov Decision Processes (MDPs). To bridge the gap between the MDP view and the methods used to specify communication protocols in multiagent systems (using logical conditions and propositional message structure), we utilise interaction frames as powerful polic...

A Modular Approach to Knowledge Transfer Between Reinforcement Learning Agents

Reinforcement learning is a general approach to learning reactive control policies. It is an unsupervised learning technique, making it a candidate for use in system that adapts to changing tasks and environment by autonomously devising a new strategy. Unfortunately, reinforcement learning methods are slow to converge to a solution, rendering them impractical in most cases. The key shortcoming ...

Knowledge Transfer in Reinforcement Learning