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Training Agents using Upside-Down Reinforcement Learning
Traditional Reinforcement Learning (RL) algorithms either predict reward...
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Learning to Prune Deep Neural Networks via Reinforcement Learning
This paper proposes PuRL - a deep reinforcement learning (RL) based algo...
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An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in ...
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Reinforcement Learning for Active Flow Control in Experiments
We demonstrate experimentally the feasibility of applying reinforcement ...
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Knowledge Sharing for Reinforcement Learning: Writing a BOOK
This paper proposes a novel deep reinforcement learning (RL) method inte...
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Solving the k-sparse Eigenvalue Problem with Reinforcement Learning
We examine the possibility of using a reinforcement learning (RL) algori...
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Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Safe reinforcement learning has many variants and it is still an open re...
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Mitigating Multi-Stage Cascading Failure by Reinforcement Learning
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates.
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