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» Using inaccurate models in reinforcement learning
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ILP
2007
Springer
14 years 1 months ago
Building Relational World Models for Reinforcement Learning
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
AAAI
2010
13 years 9 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...
IAT
2010
IEEE
13 years 5 months ago
A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge and Reinforcement Learning
Abstract--The paper proposes a biologically-inspired cognitive agent model, known as FALCON-X, based on an integration of the Adaptive Control of Thought (ACT-R) architecture and a...
Ah-Hwee Tan, Gee Wah Ng
FUZZIEEE
2007
IEEE
14 years 2 months ago
Fuzzy Approximation for Convergent Model-Based Reinforcement Learning
— Reinforcement learning (RL) is a learning control paradigm that provides well-understood algorithms with good convergence and consistency properties. Unfortunately, these algor...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
AI
1998
Springer
13 years 7 months ago
Model-Based Average Reward Reinforcement Learning
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...
Prasad Tadepalli, DoKyeong Ok