4 papers
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.…
Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning
Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to new tasks by training on data from a set of different tasks. Context-based approaches…
Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
Offline reinforcement learning (RL) allows learning sequential behavior from fixed datasets. Since offline datasets do not cover all possible situations, many methods collect addit…
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
Aidan Scannell, Kalle Kujanpää, Yi Zhao +3
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient representation learning method using only a self-sup…