Search Results for author:
Found 5 papers, 1 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior.
On Linear Convergence of Policy Gradient Methods for Finite MDPs
We revisit the finite time analysis of policy gradient methods in the one of the simplest settings: finite state and action MDPs with a policy class consisting of all stochastic policies and with exact gradient evaluations.
Global Optimality Guarantees For Policy Gradient Methods
Policy gradients methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a parameterized class of polices.
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value function corresponding to a given policy in a Markov decision process.
