From the 1 of 6 linked papers with an AI index.
6 papers
Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance
Gaspard Lambrechts, Adrien Bolland, Daniel Ebi +1
The paper introduces Reinforced Dreamer, an asymmetric model‑based reinforcement learning algorithm that uses latent guidance to improve representation learning from privileged inf…
Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access
Daniel Ebi, Damien Ernst, Klemens Böhm +1
Asymmetric reinforcement learning leverages privileged information available during training to improve learning under partial observability. Existing asymmetric actor-critic metho…
Parallelizable memory recurrent units
Florent De Geeter, Gaspard Lambrechts, Damien Ernst +1
With the emergence of massively parallel processing units, parallelization has become a desirable property for new sequence models. The ability to parallelize the processing of seq…
Maximum-Entropy Exploration with Future State-Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by providing additional intrinsic rewa…
Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
Maximum entropy reinforcement learning integrates exploration into policy learning by providing additional intrinsic rewards proportional to the entropy of some distribution. In th…
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
Gaspard Lambrechts, Damien Ernst, Aditya Mahajan
In reinforcement learning for partially observable environments, many successful algorithms have been developed within the asymmetric learning paradigm. This paradigm leverages add…