Learning with Whom to Communicate Using Relational Reinforcement Learning

Relational reinforcement learning (RRL) has emerged in the machine learning community as a new promising subfield of reinforcement learning (RL) (e.g. [1]). It upgrades RL techniques by using relational representations for states, actions and learned value-functions or policies to allow more natural representations and abstractions of complex tasks. This leads to a serious state space reduction, allowing to better generalize and infer new knowledge.

[1]  Peter Stone,et al.  Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.

[2]  Martijn van Otterlo,et al.  The logic of adaptive behavior : knowledge representation and algorithms for the Markov decision process framework in first-order domains , 2008 .

[3]  Ioan Alfred Letia,et al.  Developing Collaborative Golog Agents by Reinforcement Learning , 2002, Int. J. Artif. Intell. Tools.

[4]  Mehdi Dastani,et al.  A characterization of sapient agents , 2003, IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH37502).

[5]  Thomas Lukasiewicz,et al.  Game theoretic Golog under partial observability , 2005, AAMAS '05.

[6]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[7]  Karl Tuyls,et al.  Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics , 2004, ECML.

[8]  Eduardo F. Morales,et al.  Learning to fly by combining reinforcement learning with behavioural cloning , 2004, ICML.

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[11]  Sandip Sen,et al.  Towards a pareto-optimal solution in general-sum games , 2003, AAMAS '03.

[12]  Robert Givan,et al.  Approximate Policy Iteration with a Policy Language Bias , 2003, NIPS.

[13]  Carlos Guestrin,et al.  Generalizing plans to new environments in relational MDPs , 2003, IJCAI 2003.

[14]  Maurice Bruynooghe,et al.  Multi-agent Relational Reinforcement Learning , 2005, LAMAS.

[15]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[16]  De,et al.  Relational Reinforcement Learning , 2001, Encyclopedia of Machine Learning and Data Mining.

[17]  Karl Tuyls,et al.  Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective , 2008, J. Mach. Learn. Res..

[18]  Amal El Fallah Seghrouchni,et al.  Learning in BDI Multi-agent Systems , 2004, CLIMA.

[19]  Luc De Raedt,et al.  Bellman goes relational , 2004, ICML.

[20]  Mahesan Niranjan,et al.  On-line Q-learning using connectionist systems , 1994 .

[21]  Robert Givan,et al.  Relational Reinforcement Learning: An Overview , 2004, ICML 2004.

[22]  Craig Boutilier,et al.  Symbolic Dynamic Programming for First-Order MDPs , 2001, IJCAI.

[23]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[24]  K. Tuyls,et al.  Multi-Agent Relational Reinforcement Learning Explorations in Multi-State Coordination Tasks , 2006 .

[25]  Kurt Driessens,et al.  Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner , 2001, ECML.

[26]  Craig Boutilier,et al.  The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.

[27]  Ann Nowé,et al.  Social Agents Playing a Periodical Policy , 2001, ECML.

[28]  Michael P. Wellman,et al.  Experimental Results on Q-Learning for General-Sum Stochastic Games , 2000, ICML.

[29]  Kagan Tumer,et al.  Collective Intelligence and Braess' Paradox , 2000, AAAI/IAAI.

[30]  Ioan Alfred Letia,et al.  Developing collaborative Golog agents by reinforcement learning , 2001, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001.

[31]  Marco Wiering,et al.  Explorations in efficient reinforcement learning , 1999 .

[32]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[33]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[34]  Tom Lenaerts,et al.  A selection-mutation model for q-learning in multi-agent systems , 2003, AAMAS '03.

[35]  Saso Dzeroski,et al.  Integrating Guidance into Relational Reinforcement Learning , 2004, Machine Learning.

[36]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[37]  Sylvie Thiébaux,et al.  Exploiting First-Order Regression in Inductive Policy Selection , 2004, UAI.