Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning

Hierarchical Hybrid Reinforcement Learning Algorithms

Hierarchical Reinforcement Learning: A Hybrid Approach

In this thesis we investigate the relationships between the symbolic and subsymbolic methods used for controlling agents by artificial intelligence, focusing in particular on methods that learn. In light of the strengths and weaknesses of each approach, we propose a hybridisation of symbolic and subsymbolic methods to capitalise on the best features of each. We implement such a hybrid system, c...

Hierarchical Reinforcement Learning with Parameters

In this work we introduce and evaluate a model of Hierarchical Reinforcement Learning with Parameters. In the first stage we train agents to execute relatively simple actions like reaching or gripping. In the second stage we train a hierarchical manager to compose these actions to solve more complicated tasks. The manager may pass parameters to agents thus controlling details of undertaken acti...

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...

Concurrent Hierarchical Reinforcement Learning

We describe a language for partially specifying policies in domains consisting of multiple subagents working together to maximize a common reward function. The language extends ALisp with constructs for concurrency and dynamic assignment of subagents to tasks. During learning, the subagents learn a distributed representation of the Q-function for this partial policy. They then coordinate at run...