Reinforcement Learning
Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning
A challenging problem in seeking to bring multi-agent reinforcement learning (MARL) techniques into real-world applications, such as autonomous driving and drone swarms, is how to control multiple agents safely and cooperatively to accomplish tasks.
Going Beyond Heuristics by Imposing Policy Improvement as a Constraint Chi-Chang Lee
As such, we prevent policies from merely exploiting heuristic rewards without improving the task reward.
Diffusion Imitation from Observation
Learning from observation (LfO) aims to imitate experts by learning from state-only demonstrations without requiring action labels.
Towards Effective Planning Strategies for Dynamic Opinion Networks
Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.
Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation.
CALE: Continuous Arcade Learning Environment
We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013].
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps Benjamin Ellis University of Oxford Matthew T. Jackson
Reinforcement Learning (RL) aims to learn robust policies from an agent's experience. This has the potential for large scale real-world impact in areas such as autonomous driving or improving logistic chains.
