model-based reinforcement learning

When to Trust Your Model: Model-Based Policy Optimization NeurIPS 2019 When to use parametric models in reinforcement learning? NeurIPS 2019 Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search ICLR 2019 Adaptable replanning with compressed linear action models for learning from demonstrations CoRL 2018 Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NeurIPS 2018 Data center cooling using model-predictive control NeurIPS 2018 Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control AISTATS 2018 Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning ACL 2018 Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models NeurIPS 2018 Dual Policy Iteration NeurIPS 2018 Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation ICML 2018 Fast Model Identification via Physics Engines for Data-Efficient Policy Search IJCAI 2018 Generative Temporal Models with Spatial Memory for Partially Observed Environments ICML 2018 Iterative Value-Aware Model Learning NeurIPS 2018 Learning the Reward Function for a Misspecified Model ICML 2018 Lipschitz Continuity in Model-based Reinforcement Learning ICML 2018 Model-Based Reinforcement Learning via Meta-Policy Optimization CoRL 2018 Model-Ensemble Trust-Region Policy Optimization ICLR 2018 Object-Oriented Dynamics Predictor NeurIPS 2018 Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains IJCAI 2018 PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos ICML 2018 Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion NeurIPS 2018 Temporal Difference Models: Model-Free Deep RL for Model-Based Control ICLR 2018 TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning ICLR 2018 Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning ICML 2017