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Found 6 papers, 1 papers with code
Conditional Kernel Imitation Learning for Continuous State Environments
Classical methods such as behavioral cloning and inverse reinforcement learning are highly sensitive to estimation errors, a problem that is particularly acute in continuous state space problems.
Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers
Over the past decade, neural network (NN)-based controllers have demonstrated remarkable efficacy in a variety of decision-making tasks.
Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel
We consider the control of a Markov decision process (MDP) that undergoes an abrupt change in its transition kernel (mode).
Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks
Solar hosting capacity analysis (HCA) assesses the ability of a distribution network to host distributed solar generation without seriously violating distribution network constraints.
Designing Interpretable Approximations to Deep Reinforcement Learning
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance.
Scheduling Flexible Non-Preemptive Loads in Smart-Grid Networks
A market consisting of a generator with thermal and renewable generation capability, a set of non-preemptive loads (i. e., loads which cannot be interrupted once started), and an independent system operator (ISO) is considered.
