Abstract--In this paper, we present an agentic double deep Q-network (DDQN) scheduler for licensed/unlicensed band a l-location in New Radio (NR) sidelink (SL) networks. Beyond conventional reward-seeking reinforcement learning (RL), the agent perceives and reasons over a multi-dimensional conte xt that jointly captures queueing delay, link quality, coexistenc e intensity, and switching stability. A capacity-aware, quality of serv ice (QoS)- constrained reward aligns the agent with goal-oriented sch eduling rather than static thresholding. Under constrained bandwi dth, the proposed design reduces blocking by up to 87.5% versus thres hold policies while preserving throughput, highlighting the va lue of context-driven decisions in coexistence-limited NR SL net works. The proposed scheduler is an embodied agent (E-agent) tailo red for task-specific, resource-efficient operation at the netw ork edge.

Generation-based fuzz testing can uncover various bugs and security vulnerabilities. However, compared to mutation-based fuzz testing, it takes much longer to develop a well-balanced generator that produces good test cases and decides where to break the underlying structure to exercise new code paths. We propose a novel approach to combine a trained test case generator deep learning model with a double deep Q-network (DDQN) for the first time. The DDQN guides test case creation based on a code coverage signal. Our approach improves the code coverage performance of the underlying generator model by up to 18.5\% for the Firefox HTML rendering engine compared to the baseline grammar based fuzzer.

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network. By using Natural Evolution Strategies and a population of SE parameter vectors, we train agents in the inner loop on evolving SEs while in the outer loop we use the performance on the target task as a score for meta-updating the SE population. We show empirically that our method is capable of learning SEs for two discrete-action-space tasks (CartPole-v0 and Acrobot-v1) that allow us to train agents more robustly and with up to 60% fewer steps. Not only do we show in experiments with 4000 evaluations that the SEs are robust against hyperparameter changes such as the learning rate, batch sizes and network sizes, we also show that SEs trained with DDQN agents transfer in limited ways to a discrete-action-space version of TD3 and very well to Dueling DDQN.

This paper proposes a novel and scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, routing and spectrum access are tackled as separate tasks; further, the wireless links in the network are assumed to be fixed, and a different agent is trained for each transmission node -- this limits scalability and generalizability. In this paper, we account for the inherent signal-to-interference-plus-noise ratio (SINR) in the physical layer and propose a more scalable approach in which a single agent is associated with each flow. Specifically, a single agent makes all routing and spectrum access decisions as it moves along the frontier nodes of each flow. The agent is trained according to the physical layer characteristics of the environment using the future bottleneck SINR as a novel reward definition. This allows a highly effective routing strategy based on the geographic locations of the nodes in the wireless ad-hoc network. The proposed deep reinforcement learning strategy is capable of accounting for the mutual interference between the links. It learns to avoid interference by intelligently allocating spectrum slots and making routing decisions for the entire network in a scalable manner.