Efficient Reinforcement Learning via Probabilistic Trajectory Optimization

George I. Boutselis
George I. Boutselis

IEEE Transactions on Neural Networks and Learning Systems, pp. 5459-5474, 2018.

Cited by: 7|Views10
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Abstract:

We present a trajectory optimization approach to reinforcement learning in continuous state and action spaces, called probabilistic differential dynamic programming (PDDP). Our method represents systems dynamics using Gaussian processes (GPs), and performs local dynamic programming iteratively around a nominal trajectory in Gaussian belie...More

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