Intrinsically Motivated Exploration in Hierarchical Reinforcement ...

The acquisition of hierarchies of reusable skills is one of the distinguishing characteristics of human intelligence, and the learning of such hierarchies ...

Vigorito, Christopher M., "Intrinsically Motivated Exploration in Hierarchical Reinforcement Learning". (2016). Doctoral Dissertations. 603. https ...

A Hierarchical Take on Intrinsically Motivated Exploration - arXiv

Abstract:Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation ...

Intrinsic Motivation Based Hierarchical Exploration for Model ... - MDPI

In this work, our framework combines the intrinsic motivation-driven exploration and hierarchical exploration to accelerate model learning and hierarchical ...

Hierarchical intrinsically motivated agent planning behavior with ...

The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the ...

Hierarchical Deep Reinforcement Learning: Integrating Temporal ...

One of the key difficulties is in- sufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can ...

Learning Intrinsically Motivated Options to Stimulate Policy Exploration

This requires consistent exploration of states and action sequences to ensure the policy found is optimal. One way to motivate exploration is ...

Intrinsically Motivated Learning of Hierarchical Collections of Skills

Intrinsic motivation leads organisms to en- gage in exploration, play, and other behavior driven by cu- riosity in the absence of explicit reward. In a classic ...

Efficient Exploration through Intrinsic Motivation Learning for ... - arXiv

Efficient exploration for automatic subgoal dis- covery is a challenging problem in Hierarchical. Reinforcement Learning (HRL). In this paper, ...

Feature Control as Intrinsic Motivation for Hierarchical ... - IEEE Xplore

Abstract: One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency problem, which stems both from an inability ...

Hierarchical Deep Reinforcement Learning: Integrating Temporal ...

One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore ...

A Hierarchical Take on Intrinsically Motivated Exploration

Exploration in sparse reward reinforcement learn- ing remains an open challenge. Many state-of- the-art methods use intrinsic motivation to com- plement the ...

A Hierarchical Take on Intrinsically Motivated Exploration

Exploration in sparse reward reinforcement learning remains an open ... Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning.

Hierarchical intrinsically motivated agent planning behavior with ...

The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the extrinsic signal, and ...

Intrinsically Motivated Reinforcement Learning - CS@Cornell

the ideas of exploration and shaping bonuses [6, 10], although our ... Intrinsically motivated learning of hierarchical collec- tions of skills. In ...

Intrinsically motivated learning of hierarchical collections of skills

... Individuals motivated by intrinsic factors engage in novel acts without expecting a reward. As a result, intrinsic motivation positively affects learning ...

A Hierarchical Take on Intrinsically Motivated Exploration

Abstract: Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic ...

Integrating Temporal Abstraction and Intrinsic Motivation

Citation: Kulkarni, Tejas D. et al. "Hierarchical Deep Reinforcement Learning: Integrating. Temporal Abstraction and Intrinsic Motivation.

[PDF] Feature Control as Intrinsic Motivation for Hierarchical ...

A DRL algorithm that aims to improve data efficiency via both the utilization of unrewarded experiences and the exploration strategy by combining ideas from ...

Selective Exploration Exploiting Skills in Hierarchical Reinforcement ...

Abstract— In this paper, novel reinforcement learning method with intrinsic motivation for reproducibility of the past suc- cessful experience is presented.