3 citations · 7 across the 13 of their papers we have counts for
15 papers
Continual Graph Multi-Agent Reinforcement Learning
Tommaso Marzi, Ahmed Hendawy, Jan Peters +3
In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new tasks while preserving the…
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…
The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
Tobias Lademann, Théo Vincent, Jan Peters +1
Reinforcement learning has shown promising results for optimizing the control of industrial energy systems, yet most existing studies remain limited to the application in simulatio…
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…
Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
Hamish Flynn, Joe Watson, Ingmar Posner +1
We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm. Posterior sampling is a heuristic for decision-making under un…
Gradient Iterated Temporal-Difference Learning
Théo Vincent, Kevin Gerhardt, Yogesh Tripathi +5
Temporal-difference (TD) learning is highly effective at controlling and evaluating an agent's long-term outcomes. Most approaches in this paradigm implement a semi-gradient update…
Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning
Ahmed Hendawy, Henrik Metternich, Théo Vincent +3
The use of target networks is a popular approach for estimating value functions in deep Reinforcement Learning (RL). While effective, the target network remains a compromise soluti…
-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…
Eau De -Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
Théo Vincent, Tim Faust, Yogesh Tripathi +2
Recent works have successfully demonstrated that sparse deep reinforcement learning agents can be competitive against their dense counterparts. This opens up opportunities for rein…
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential e…
Adaptive -Network: On-the-fly Target Selection for Deep Reinforcement Learning
Théo Vincent, Fabian Wahren, Jan Peters +2
Deep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand.…
Iterated -Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
Théo Vincent, Daniel Palenicek, Boris Belousov +2
The vast majority of Reinforcement Learning methods is largely impacted by the computation effort and data requirements needed to obtain effective estimates of action-value functio…
Parameterized Projected Bellman Operator
Théo Vincent, Alberto Maria Metelli, Boris Belousov +3
Approximate value iteration (AVI) is a family of algorithms for reinforcement learning (RL) that aims to obtain an approximation of the optimal value function. Generally, AVI algor…
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)…
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
Ahmed Hendawy, Jan Peters, Carlo D'Eramo
Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing represe…