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Found 8 papers, 2 papers with code
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Entropy regularization, smoothing of Q-values and neural network function approximator are key components of the state-of-the-art reinforcement learning (RL) algorithms, such as Soft Actor-Critic~\cite{haarnoja2018soft}.
On the Convergence of Approximate and Regularized Policy Iteration Schemes
Entropy regularized algorithms such as Soft Q-learning and Soft Actor-Critic, recently showed state-of-the-art performance on a number of challenging reinforcement learning (RL) tasks.
Distributionally Robust Counterfactual Risk Minimization
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem.
Distributionally Robust Reinforcement Learning
Our formulation results in a efficient algorithm that accounts for a simple re-weighting of policy actions in the standard policy iteration scheme.
Action-conditional Sequence Modeling for Recommendation
In this paper, we consider interactions triggered by the recommendations of deployed recommender system in addition to browsing behavior.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon.
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user state at recommendation time.
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata.
