The paper presents a constrained command‑conditioned Proximal Policy Optimization (PPO) executor for the MicroRTS real‑time strategy environment, enabling a single execution policy to follow diverse strategic commands. A Thompson‑sampling bandit strategist selects command tuples based on in‑game observations of the opponent’s strategy, rather than relying on opponent identity. In controlled experiments, the strategist‑executor system outperforms a flat PPO baseline against three of the four strongest opponents on a training map, including two held‑out opponents, while showing no significant difference against the remaining opponents.
By Nick Leenders, Roy Lindelauf, Joost van Oijen, Boris Cule
arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).
By Logan Luna (Georgia Institute of Technology), Juan Ortiz Couder (Embry-Riddle Aeronautical University), Raul Alejandro Vargas-Acosta (Embry-Riddle Aeronautical University)
arXiv:2610.41639v1 Announce Type: new
Abstract: Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints invol...
By Chlo\'e Carer (LGI), Adam Abdin (LGI)
arXiv:2608. 15372v1 Announce Type: new Abstract: We study generating game-theoretically optimized Courses of Action (COAs) for a Blue UAS swarm against an adaptive Red adversary in a communication-degraded environment, motivated by (but not derived from) a public U.
By Phillip Jiang
arXiv:2608. 01425v1 Announce Type: cross Abstract: Training LLM-based multi-agent systems with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that such systems should be judged by their behavior, not only their reward.
By Yi Mao, Andrew Perrault
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.
By Christoph Dann, Yishay Mansour, Mehryar Mohri
arXiv:2610.41647v1 Announce Type: new
Abstract: Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome vari...
By Adam Abdin (LGI)
The paper investigates the problem of sharing a single critic across multiple parallel environments in reinforcement learning. It shows that when environments assign different expected returns to the same state, a shared critic must reconcile conflicting value targets, which can distort advantage estimates and misguide policy updates. The authors propose a simple fix—providing the critic with the environment index—demonstrating through bandit models and experiments on CartPole, MuJoCo, BipedalWalker, and 16 Procgen games that this conditional critic stabilizes learning and boosts returns, achieving a 40.8% improvement in aggregate normalized return on unseen levels.
By Zhenya Liu, Yang Meng, Zhuokai Zhao, Xuefeng Liu, Yuxin Chen
arXiv:2402.03741v4 Announce Type: replace-cross
Abstract: Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast application prospects, such as swarm control of drones,...
By Oubo Ma, Yuwen Pu, Linkang Du, Yang Dai, Ruo Wang, Xiaolei Liu, Yingcai Wu, Shouling Ji
The paper presents a novel dimensionality‑reduction framework for reach‑avoid games involving unmanned aerial vehicles. By decomposing the three‑dimensional problem into horizontal and vertical sub‑games, each solved with Hamilton‑Jacobi reachability analysis, the authors reconstruct the full solution using a tracking control algorithm that guarantees attacker capture and subsequent tracking. Numerical simulations and Gazebo simulations confirm that the decomposition preserves optimality and achieves successful quadrotor capture in three‑dimensional space.
By Minh Bui, Simon Monckton, Mo Chen
The paper introduces Regret-Weighted Payoff Sampling (RWPS), a budgeted estimator that selectively simulates only payoff-matrix cells relevant to a Nash equilibrium and uses a surrogate model for the remaining entries. RWPS provides an instance-dependent error bound weighted by the opponent’s equilibrium mixture and a coverage result guaranteeing that, once the deviation-relevant set is simulated, surrogate error does not affect either player’s regret. Experiments on three 21×21 general-sum games, including an asymmetric Colonel Blotto, show that RWPS achieves four to six times tighter bounds than previous methods and outperforms other sampling strategies on the CyGym and ANSG cyber simulators at low budgets.
By Michael Lanier, David Farmer, Yevgeniy Vorobeychik
arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.
By Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Tim Wang, Wei Zhan