8 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…
Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation
Luca Ghisi, Jacopo Essenziale, Carlo D'Eramo +1
Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes introduce substantially greater co…
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
Zechu Li, Yufeng Jin, Xiaoyang Liu +4
Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pip…
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…
Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
Sebastian Griesbach, Carlo D'Eramo
Numerous heuristics and advanced approaches have been proposed for exploration in different settings for deep reinforcement learning. Noise-based exploration generally fares well w…
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…
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
Contact Energy Based Hindsight Experience Prioritization
Erdi Sayar, Zhenshan Bing, Carlo D'Eramo +2
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences. Recen…