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Found 6 papers, 0 papers with code
Sequential annotations for naturally-occurring HRI: first insights
We explain the methodology we developed for improving the interactions accomplished by an embedded conversational agent, drawing from Conversation Analytic sequential and multimodal analysis.
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).
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
ELSIM: End-to-end learning of reusable skills through intrinsic motivation
Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improve over a baseline both transfer learning and exploration when rewards are sparse.
A survey on intrinsic motivation in reinforcement learning
In this article, we provide a survey on the role of intrinsic motivation in DRL.
TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante
This paper presents a new algorithm: TSRuleGrowth, looking for partially-ordered rules over a time series.
