Reinforcement Learning
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
In this vein of research, Reid et al. studied the effect of pre-training of Transformers on offline reinforcement learning (offline RL) tasks [
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization Songtao Lu
Classical bilevel optimization is referred to as the case where there is no consensus constraint but with only two levels of the minimization subproblems, i.e.,
MobILE: Model-Based Imitation Learning From Observation Alone
We also show that the ILFO problem is strictly harder than the standard IL problem by presenting an exponential sample complexity separation between IL and ILFO.
MobILE: Model-Based Imitation Learning From Observation Alone
We also show that the ILFO problem is strictly harder than the standard IL problem by presenting an exponential sample complexity separation between IL and ILFO.
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models
Recently, theoretical understanding of OPE has been rapidly advanced under (approximate) realizability assumptions, i.e., where the environments of interest are well approximated with the given hypothetical models.
AT details
The best method, PackNet, has forward transfer close to Fine-tuning, which is a strong baseline for this metric.
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Evaluation of CL methods is challenging.
