5 citations · 6 across the 3 of their papers we have counts for
3 papers · 1 filter
Discrete Codebook World Models for Continuous Control
Aidan Scannell, Mohammadreza Nakhaei, Kalle Kujanpää +4
In reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future outcomes in order to make informed decisions.…
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…