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Found 7 papers, 1 papers with code
A policy gradient approach for optimization of smooth risk measures
We propose policy gradient algorithms for solving a risk-sensitive reinforcement learning (RL) problem in on-policy as well as off-policy settings.
Policy Gradient Methods for Distortion Risk Measures
We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework.
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint
From these results, we infer that the first algorithm converges at a rate that is comparable to the well-known REINFORCE algorithm in an off-policy RL context, while the second algorithm exhibits an improved rate of convergence.
Non-asymptotic bounds for stochastic optimization with biased noisy gradient oracles
We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter.
Correlated bandits or: How to minimize mean-squared error online
Motivated by such applications, we formulate the correlated bandit problem, where the objective is to find the arm with the lowest mean-squared error (MSE) in estimating all the arms.
Random directions stochastic approximation with deterministic perturbations
We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA) [17], and propose new first-order and second-order algorithms.
Policy Gradients for CVaR-Constrained MDPs
We study a risk-constrained version of the stochastic shortest path (SSP) problem, where the risk measure considered is Conditional Value-at-Risk (CVaR).
