3 papers
cs.LG2025
-Level Policy Gradients for Multi-Agent Reinforcement Learning
Aryaman Reddi, Gabriele Tiboni, Jan Peters +1
Actor-critic algorithms for deep multi-agent reinforcement learning (MARL) typically employ a policy update that responds to the current strategies of other agents. While being str…
cs.RO2025
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning
Jose-Luis Holgado-Alvarez, Aryaman Reddi, Carlo D'Eramo
Reinforcement Learning (RL) has proven largely effective in obtaining stable locomotion gaits for legged robots. However, designing control algorithms which can robustly navigate u…
cs.LG2025
Deep Learning Agents Trained For Avoidance Behave Like Hawks And Doves
Aryaman Reddi
We present heuristically optimal strategies expressed by deep learning agents playing a simple avoidance game. We analyse the learning and behaviour of two agents within a symmetri…