Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed DivOE.

In all cases, HypGRU achieves the best results when the data are projected to hyperbolic spaces before theyare fed to the network, and all its layers are based on hyperbolic geometry. Results of these networks are obtained using their official code.3,4 We also evaluate a light version of Shift-GCN referred to as Shift-GCN-light, where the numbers of inputand output channels for the input and residual blocks arereduced byafactor of2(thenumber ofinput channels fortheinput block is3). We can also see that whenM = 3, GyroAI-HAUNet outperforms Shift-GCN-light on all the datasets. Overall, whenM = 3, GyroAI-HAUNet is competitive to the best GNN model with far fewer parameters.

Solving complex planning problems has been a long-standing challenge in computer science.

We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility. We propose two provably efficient model-free algorithms, Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ). These algorithms implement a form of risk-sensitive optimism in the face of uncertainty, which adapts to both riskseeking and risk-averse modes of exploration.

However, to obtain the desired effect, the step-size should be chosen sufficiently large, a task which is problem dependent andcanbedifficultinpractice.