2 citations · 6 across the 8 of their papers we have counts for
9 papers
Reinforcement Learning with Segment Feedback
Yihan Du, Anna Winnicki, Gal Dalal +2
Standard reinforcement learning (RL) assumes that an agent can observe a reward for each state-action pair. However, in practical applications, it is often difficult and costly to…
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
Yihan Du, Anna Winnicki, Gal Dalal +2
Reinforcement Learning from Human Feedback (RLHF) has achieved impressive empirical successes while relying on a small amount of human feedback. However, there is limited theoretic…
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games
Anna Winnicki, R. Srikant
Optimal policies in standard MDPs can be obtained using either value iteration or policy iteration. However, in the case of zero-sum Markov games, there is no efficient policy iter…
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation
Anna Winnicki, R. Srikant
A common technique in reinforcement learning is to evaluate the value function from Monte Carlo simulations of a given policy, and use the estimated value function to obtain a new…
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation
Anna Winnicki, R. Srikant
We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation alg…
The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation
Anna Winnicki, Joseph Lubars, Michael Livesay +1
Function approximation is widely used in reinforcement learning to handle the computational difficulties associated with very large state spaces. However, function approximation in…
Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure
Joseph Lubars, Anna Winnicki, Michael Livesay +1
We consider Markov Decision Processes (MDPs) in which every stationary policy induces the same graph structure for the underlying Markov chain and further, the graph has the follow…
On Privatizing Equilibrium Computation in Aggregate Games over Networks
Shripad Gade, Anna Winnicki, Subhonmesh Bose
We propose a distributed algorithm to compute an equilibrium in aggregate games where players communicate over a fixed undirected network. Our algorithm exploits correlated perturb…
Pricing Economic Dispatch with AC Power Flow via Local Multipliers and Conic Relaxation
Mariola Ndrio, Anna Winnicki, Subhonmesh Bose
We analyze pricing mechanisms in electricity markets with AC power flow equations that define a nonconvex feasible set for the economic dispatch problem. Specifically, we consider…