Reinforcement learning (RL) interacts with the environment to solve sequential decision- making problems via a trial-and-error approach. Errors are always undesirable in real-world …
Recently, deep reinforcement learning (DRL) methods have achieved impressive performance on tasks in a variety of domains. However, neural network policies produced …
This article is about deep learning (DL) and deep reinforcement learning (DRL) works applied to robotics. Both tools have been shown to be successful in delivering data-driven …
Abstract Context Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called “safety-critical” systems such as automotive or …
In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …
One vision of a future artificial intelligence (AI) is where many separate units can learn independently over a lifetime and share their knowledge with each other. The synergy …
Q Wang, Y Lv, Z Xie, J Huang - Advances in Neural …, 2024 - proceedings.neurips.cc
Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous methods employ the empirical risk minimization principle in optimization. However, the …
Z Peng, Q Li, C Liu, B Zhou - Conference on Robot Learning, 2022 - proceedings.mlr.press
When learning common skills like driving, beginners usually have domain experts standing by to ensure the safety of the learning process. We formulate such learning scheme under …
We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy. The baseline policy …