103 citations · 192 across the 31 of their papers we have counts for
5 papers · 1 filter
Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
Yifan Zhou, Sachin Grover, Mohamed El Mistiri +7
Reinforcement Learning (RL) traditionally relies on scalar reward signals, limiting its ability to leverage the rich semantic knowledge often available in real-world tasks. In cont…
Local Repair of Neural Networks Using Optimization
Keyvan Majd, Siyu Zhou, Heni Ben Amor +2
In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties. We formulate the properties as a set of predicates th…
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
Kevin Sebastian Luck, Mel Vecerik, Simon Stepputtis +2
Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of determ…
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra
Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted wi…
Information Maximizing Exploration with a Latent Dynamics Model
Trevor Barron, Oliver Obst, Heni Ben Amor
All reinforcement learning algorithms must handle the trade-off between exploration and exploitation. Many state-of-the-art deep reinforcement learning methods use noise in the act…