Efficient Reinforcement Learning via Probabilistic Trajectory Optimization
نویسندگان
چکیده
منابع مشابه
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
Robotic systems must be able to quickly and robustly make decisions when op-erating in uncertain and dynamic environments. While Reinforcement Learning(RL) can be used to compute optimal policies with little prior knowledge about theenvironment, it suffers from slow convergence. An alternative approach is ModelPredictive Control (MPC), which optimizes policies quickly, but also ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2018
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2017.2764499