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Found 5 papers, 0 papers with code
Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging
Thus, to model and optimize EV charging, it is important for charging station operator to model the PBDR patterns of EV customers by precisely predicting charging demands given price signals.
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations
Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations.
Laxity-Aware Scalable Reinforcement Learning for HVAC Control
Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills.
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU.
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts
Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment.
Generative Adversarial Network
Model-based Reinforcement Learning
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