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ICML
1999
IEEE
15 years 10 months ago
Implicit Imitation in Multiagent Reinforcement Learning
Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively obs...
Bob Price, Craig Boutilier
ATAL
2007
Springer
15 years 3 months ago
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
This paper presents the dynamics of multiple reinforcement learning agents from an Evolutionary Game Theoretic (EGT) perspective. We provide a Replicator Dynamics model for tradit...
Liviu Panait, Karl Tuyls
AAAI
2008
15 years 1 days ago
Efficient Learning of Action Schemas and Web-Service Descriptions
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
Thomas J. Walsh, Michael L. Littman
76
Voted
AAAI
2010
14 years 11 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
KCAP
2009
ACM
15 years 4 months ago
Interactively shaping agents via human reinforcement: the TAMER framework
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
W. Bradley Knox, Peter Stone