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Found 8 papers, 1 papers with code
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs).
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization
In robust Markov decision processes (RMDPs), it is assumed that the reward and the transition dynamics lie in a given uncertainty set.
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-making that is robust to perturbations on the transition kernel.
Policy Gradient for Rectangular Robust Markov Decision Processes
We provide a closed-form expression for the worst occupation measure.
An Efficient Solution to s-Rectangular Robust Markov Decision Processes
We present an efficient robust value iteration for \texttt{s}-rectangular robust Markov Decision Processes (MDPs) with a time complexity comparable to standard (non-robust) MDPs which is significantly faster than any existing method.
Policy Gradient for Reinforcement Learning with General Utilities
The policy gradient theorem proves to be a cornerstone in Linear RL due to its elegance and ease of implementability.
Efficient Policy Iteration for Robust Markov Decision Processes via Regularization
But we don't have a clear understanding to exploit this equivalence, to do policy improvement steps to get the optimal value function or policy.
The Geometry of Robust Value Functions
The key of this perspective is to decompose the value space, in a state-wise manner, into unions of hypersurfaces.
