Expert Q-learning: Deep Q-learning With State Values From Expert Examples. - Li Meng 002; Anis Yazidi; Morten Goodwin; Paal Engelstad (CoRR, 2021)

Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples. - Li Meng 002; Anis Yazidi; Morten Goodwin; Paal Engelstad (NLDL, 2022)

Improving the Diversity of Bootstrapped DQN via Noisy Priors. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CoRR, 2022)

Deep Reinforcement Learning with Swin Transformer. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CoRR, 2022)

Unsupervised State Representation Learning in Partially Observable Atari Games. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CAIP (2), 2023)

Unsupervised Representation Learning in Partially Observable Atari Games. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CoRR, 2023)

Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal E. Engelstad (IEEE Trans. Games, 2023)

A Manifold Representation of the Key in Vision Transformers. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CGI (2), 2024)

Deep Reinforcement Learning with Swin Transformers. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (ICDSP, 2024)

State Representation Learning Using an Unbalanced Atlas. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal E. Engelstad (ICLR, 2024)

Maximum Manifold Capacity Representations in State Representation Learning. - Li Meng 002; Morten Goodwin; Anis Yazidi; Paal Engelstad (CoRR, 2024)