From the 1 of 7 linked papers with an AI index.
7 papers
Dynamics Models for Offline Hyperparameter Selection in Real-World RL
Jordan Coblin, Han Wang, Martha White +1
A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and online experimentation is cos…
Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners
Haseeb Shah, Lingwei Zhu, Adam White +1
The paper empirically evaluates how design choices in actor‑critic reinforcement learning algorithms affect performance and robustness on a real‑world water‑treatment control task,…
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…
Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
Jiamin He, Samuel Neumann, Jincheng Mei +2
Mixture policies theoretically offer greater flexibility than unimodal policies in continuous action reinforcement learning, but the practical benefits of this complexity remain el…
Forager: a lightweight testbed for continual learning with partial observability in RL
Steven Tang, Xinze Xiong, Anna Hakhverdyan +7
In continual reinforcement learning (CRL), good performance requires never-ending learning, acting, and exploration in a big, partially observable world. Most CRL experiments have…
Deep Reinforcement Learning with Gradient Eligibility Traces
Esraa Elelimy, Brett Daley, Andrew Patterson +3
Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for t…