policy search

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning NeurIPS 2020 Gradient-Aware Model-Based Policy Search AAAI 2020 Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation UAI 2020 High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization NeurIPS 2020 Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework AAAI 2020 Learning to Model Opponent Learning (Student Abstract) AAAI 2020 Population-Guided Parallel Policy Search for Reinforcement Learning ICLR 2020 Preference-based Reinforcement Learning with Finite-Time Guarantees NeurIPS 2020 Safe non-smooth black-box optimization with application to policy search L4DC 2020 AutoAugment: Learning Augmentation Strategies From Data CVPR 2019 Automatically Learning Data Augmentation Policies for Dialogue Tasks EMNLP 2019 Bayesian Optimization for Policy Search via Online-Offline Experimentation JMLR 2019 BayesSim: Adaptive Domain Randomization Via Probabilistic Inference for Robotics Simulators RSS 2019 CEM-RL: Combining evolutionary and gradient-based methods for policy search ICLR 2019 Deep Reactive Policies for Planning in Stochastic Nonlinear Domains AAAI 2019 Guided Meta-Policy Search NeurIPS 2019 Transfer of Samples in Policy Search via Multiple Importance Sampling ICML 2019 Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search ICLR 2019 A Block Coordinate Ascent Algorithm for Mean-Variance Optimization NeurIPS 2018 Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment CoRL 2018 Efficient Gradient-Free Variational Inference using Policy Search ICML 2018 Expanding Motor Skills using Relay Networks CoRL 2018 Fast Model Identification via Physics Engines for Data-Efficient Policy Search IJCAI 2018 Meta-Reinforcement Learning of Structured Exploration Strategies NeurIPS 2018 No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling ACL 2018