Markov Models
Causal Imitation for Markov Decision Processes: a Partial Identification Approach
Children often learn how to behave in an unfamiliar environment by imitating adults. Imitation learning (IL) enables a learning agent to behave in an unknown environment by observing expert demonstrations.
ACTOR S
We also demonstrate its effectiveness in generating robotic tasks.
Deep Learning for Computing Convergence Rates of Markov Chains
Markovian models, estimating the rate of convergence to equilibrium is critical.
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q-pi Realizability and Concentrability
The hope in this setting is that learning a good policy will be possible without requiring a sample size that scales with the number of states in the MDP . Foster et al. [ 2021 ] have shown this to be impossible even under concentrability, a data coverage assumption where a coefficient C
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
We introduce the first sample-efficient algorithm for LMDPs without any additional distributional assumptions . Our result builds off a new perspective on the role of off-policy evaluation guarantees and coverage coefficients in LMDPs, a perspective, that has been overlooked in the context of exploration in partially observed environments.
3D Gaussian Splatting as Markov Chain Monte Carlo
To do so, we revise the'cloning' of Gaussians
Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling methods. In this work, we propose to train discrete EBMs with Energy Discrepancy, a loss function which only requires the evaluation of the energy function at data points and their perturbed counterparts, thus eliminating the need for Markov chain Monte Carlo.
