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Reinforcement learning approaches in social robotics
This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …
learning is a framework for decision-making problems in which an agent interacts through …
An information-theoretic perspective on intrinsic motivation in reinforcement learning: A survey
The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …
new contributions, especially considering the emergent field of deep RL (DRL). However, a …
Model-based reinforcement learning: A survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
Technological approach to mind everywhere: an experimentally-grounded framework for understanding diverse bodies and minds
Synthetic biology and bioengineering provide the opportunity to create novel embodied
cognitive systems (otherwise known as minds) in a very wide variety of chimeric …
cognitive systems (otherwise known as minds) in a very wide variety of chimeric …
Variational intrinsic control
In this paper we introduce a new unsupervised reinforcement learning method for
discovering the set of intrinsic options available to an agent. This set is learned by …
discovering the set of intrinsic options available to an agent. This set is learned by …
A survey on intrinsic motivation in reinforcement learning
The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …
new contributions; especially considering the emergent field of deep RL (DRL). However a …
Variational information maximisation for intrinsically motivated reinforcement learning
The mutual information is a core statistical quantity that has applications in all areas of
machine learning, whether this is in training of density models over multiple data modalities …
machine learning, whether this is in training of density models over multiple data modalities …
Surprise-based intrinsic motivation for deep reinforcement learning
Exploration in complex domains is a key challenge in reinforcement learning, especially for
tasks with very sparse rewards. Recent successes in deep reinforcement learning have …
tasks with very sparse rewards. Recent successes in deep reinforcement learning have …
[کتاب][B] Deep reinforcement learning
A Plaat - 2022 - Springer
Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …
achieved in activities as diverse as autonomous driving, game playing, molecular …
Active learning of inverse models with intrinsically motivated goal exploration in robots
We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity
(SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which …
(SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which …