Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning.

Recent studies in reinforcement learning have explored brain-inspired function approximators and learning algorithms to simulate brain intelligence and adapt to neuromorphic hardware. Among these approaches, reward-modulated spike-timing-dependent plasticity (R-STDP) is biologically plausible and energy-efficient, but suffers from a gap between its local learning rules and the global learning objectives, which limits its performance and applicability. In this paper, we design a recurrent winner-take-all network and propose the spiking variational policy gradient (SVPG), a new R-STDP learning method derived theoretically from the global policy gradient.

Numerical Simulation and Experimental Study of Carbon Fiber-Reinforced Polymer Single-Bar Extrusion Anchorage Structure.

The reliable anchorage of carbon fiber-reinforced polymer (CFRP) tendons is a critical issue influencing the stable bearing capacity of bridge cables. This study introduces a novel CFRP single-strand extrusion anchoring structure, where the strand is compressed at its end. By integrating this with internal cone filler wrapping, we create a CFRP multi-strand cable composite anchoring system.

Adjacency Constraint for Efficient Hierarchical Reinforcement Learning.

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e.

Partial Consistency for Stabilizing Undiscounted Reinforcement Learning.

Undiscounted return is an important setup in reinforcement learning (RL) and characterizes many real-world problems. However, optimizing an undiscounted return often causes training instability. The causes of this instability problem have not been analyzed in-depth by existing studies.

LINC01615 activates ZEB2 through competitively binding with miR-3653-3p to promote the carcinogenesis of colon cancer cells.

As a newly discovered cancer-related molecule, we explored the unreported mechanism of LINC01615 intervention in colon cancer.LINC01615 expression in clinical samples and cells were detected. Effects of LINC01615 silencing/overexpression on the malignant development of colon cancer cells were analyzed through cell function experiments.

Orientation-Preserving Rewards' Balancing in Reinforcement Learning.

Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewards on pursuing the main rewards, which leads to the destruction of the optimal policy. Thus, it is challenging but essential to balance the main and auxiliary rewards.

State-Temporal Compression in Reinforcement Learning With the Reward-Restricted Geodesic Metric.

It is difficult to solve complex tasks that involve large state spaces and long-term decision processes by reinforcement learning (RL) algorithms. A common and promising method to address this challenge is to compress a large RL problem into a small one. Towards this goal, the compression should be state-temporal and optimality-preserving (i.

Generative Memory for Lifelong Learning.

Lifelong learning is a crucial issue in advanced artificial intelligence. It requires the learning system to learn and accumulate knowledge from sequential tasks. The learning system needs to deal with increasingly more domains and tasks.

Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All.

Hidden Markov models (HMMs) underpin the solution to many problems in computational neuroscience. However, it is still unclear how to implement inference of HMMs with a network of neurons in the brain. The existing methods suffer from the problem of being nonspiking and inaccurate.

Hierarchical Bayesian Inference and Learning in Spiking Neural Networks.

Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. However, it remains unknown how such a computation is organized in the network of biologically plausible spiking neurons.