Function approximation-based reinforcement learning for large-scale problem domains
Reinforcement learning (RL) encounters the increasing challenge of maintaining good performance in emerging large-scale real-world problems. Function approximation is the key technique to solve the performance degradation issues when implementing RL algorithms in problems with continuous and/or larg...
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| Online Access: | http://hdl.handle.net/2047/D20317961 |