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Carbon emissions and sustainability of launching 5G mobile networks in China
We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23. 82 +- 1. 07 megatons in China.
Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning
More specifically, we evaluate the effect of an imaginary transition by calculating the change of the loss computed on the real samples when we use the transition to train the action-value and policy functions.
Model-based Reinforcement Learning
reinforcement-learning
+1
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma.
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications.
