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ROBOCUP
2007
Springer

Model-Based Reinforcement Learning in a Complex Domain

10 years 4 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the environment. Model-free algorithms perform updates solely bas ed on observed experiences. By contrast, model-based algorithms learn a model of the environment that effectively simulates its dynamics. The model may be used to simulate experiences or to plan into the future, potentially expediting the learning process. This paper presents a model-based reinforcement learning approach for Keepaway, a complex, continuous, stochastic, multiagent subtask of RoboCup simulated soccer. First, we propose the design of an environmental model that is partly learned based on the agent’s experiences. This model is then coupled with the reinforcement learning algorithm to learn an action selection policy. We evaluate our method through empirical comparisons with model-free approaches that have been previously applied succe...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where ROBOCUP
Authors Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
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