1 citations · 1 across the 5 of their papers we have counts for
6 papers
BluffJAX: Adversarial Imperfect Information Games in JAX
Aryaman Reddi, Jan Peters, Carlo D'Eramo
We introduce BluffJAX: an open-source suite of adversarial imperfect information games in JAX. We provide canonical implementations of games designed for high simulation throughput…
Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
Noah Farr, Aryaman Reddi, Carlo D'Eramo +1
Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.e. with…
-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…
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…
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…
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Aryaman Reddi, Maximilian Tölle, Jan Peters +2
Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL)…