10 papers
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…
The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
Tobias Lademann, Théo Vincent, Théo Vincent +2
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…
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…
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…
-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…