Recently,Transformer architecture hasbeenintroduced intoimagerestoration to replace convolution neural network (CNN) withsurprising results.

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.

Game Theory: Is legal doping in sport a good idea? As the Winter Games celebrate the Olympic motto, Faster, Higher, Stronger -- Together, a new competition is openly allowing the use of performance-enhancing drugs. Samantha Johnson looks at the Enhanced Games and how doping, once sport's red line, is now being marketed as innovation. AFCON: To walk or not to walk?

With the disentangled representations, we synthesize the counterfactual unbiased training samples to further decorrelate causal and bias variables.

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents incomplexenvironments. Toaddress these issues, we propose RMIX, anovelcooperativeMARL method with theConditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaRfordecentralized execution. Then,tohandle thetemporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictorforriskleveltuning.