3 citations · 3 across the 5 of their papers we have counts for
5 papers
Model-Based Reinforcement Learning under Random Observation Delays
Armin Karamzade, Kyungmin Kim, JB Lanier +2
Delays frequently occur in real-world environments, yet standard reinforcement learning (RL) algorithms often assume instantaneous perception of the environment. We study random se…
Adapting World Models with Latent-State Dynamics Residuals
JB Lanier, Kyungmin Kim, Armin Karamzade +5
Simulation-to-reality reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade ag…
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Kyungmin Kim, JB Lanier, Pierre Baldi +2
Recent advancements in Model-Based Reinforcement Learning (MBRL) have made it a powerful tool for visual control tasks. Despite improved data efficiency, it remains challenging to…
Reinforcement Learning from Delayed Observations via World Models
Armin Karamzade, Kyungmin Kim, Montek Kalsi +1
In standard reinforcement learning settings, agents typically assume immediate feedback about the effects of their actions after taking them. However, in practice, this assumption…
Moonwalk: Inverse-Forward Differentiation
Dmitrii Krylov, Armin Karamzade, Roy Fox
Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a…