SOLVING ACTION SEMANTIC CONFLICT IN PHYSICALLY HETEROGENEOUS MULTI-AGENT REINFORCEMENT LEARNING WITH GENERALIZED ACTION-PREDICTION OPTIMIZATION

Solving Action Semantic Conflict in Physically Heterogeneous Multi-Agent Reinforcement Learning with Generalized Action-Prediction Optimization

Traditional multi-agent reinforcement learning (MARL) algorithms typically implement global parameter sharing across various types of heterogeneous agents without meticulously differentiating between different action semantics.This approach results in the action semantic conflict problem, which decreases the generalization ability of policy network

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Recovering rearranged cancer chromosomes from karyotype graphs

Abstract Background Many cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes.Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome.Recently it became possible to reconcile both of these types of large-scale variations into a karyoty

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