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Reinforcement learning

Known as: RL, Actor critic architecture, Reward function 
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in… 
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Related topics

Related topics

50 relations
AIXIAction selectionAndrew BartoAnticipation (artificial intelligence)

Broader (1)

Belief revision

Papers overview

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Highly Cited
2016
Highly Cited
2016
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN… 
Highly Cited
2011
Highly Cited
2011
Bayesian Multitask Inverse Reinforcement Learning
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may… 
Highly Cited
2010
Highly Cited
2010
Multi-Agent Inverse Reinforcement Learning
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications… 
Highly Cited
2004
Highly Cited
2004
Reinforcement learning with replacing eligibility traces
The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we… 
Highly Cited
2004
Highly Cited
2004
Reinforcement learning for reactive power control
This paper presents a Reinforcement Learning (RL) method for network constrained setting of control variables. The RL method… 
Highly Cited
2003
Highly Cited
2003
Reinforcement learning for true adaptive traffic signal control
The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation… 
Highly Cited
2001
Highly Cited
2001
Stock price prediction using reinforcement learning
  • Jae Won Lee
  • ISIE . IEEE International Symposium on…
  • 2001
  • Corpus ID: 14043536
Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to… 
Highly Cited
1998
Highly Cited
1998
Tree Based Discretization for Continuous State Space Reinforcement Learning
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its… 
Highly Cited
1998
Highly Cited
1998
Theoretical Results on Reinforcement Learning with Temporally Abstract Options
We present new theoretical results on planning within the framework of temporally abstract reinforcement learning (Precup… 
Highly Cited
1994
Highly Cited
1994
Acquiring robot skills via reinforcement learning
Skill acquisition is a difficult , yet important problem in robot performance. The authors focus on two skills, namely robotic… 
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