In addition, by analyzing the heatmap of priority changes at various locations in the priority memory during training, we find that memory size and rollout length can have a significant impact on the distribution of trajectory priorities and, hence, on the performance of the algorithm.
The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs.
We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.
Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated.
Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction.
Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks.