model-based planning

TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification NeurIPS 2022 “Think Before You Speak”: Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs IJCAI 2022 Artificial Intelligence and Machine Learning for Autonomous Agents that Learn to Plan and Operate in Unpredictable Dynamic Environments AAAI 2021 Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization ICLR 2021 Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning CoRL 2021 CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models AAAI 2021 Deep Residual Reinforcement Learning (Extended Abstract) IJCAI 2021 Generating High-Quality Explanations for Navigation in Partially-Revealed Environments NeurIPS 2021 Learning How to Solve “Bubble Ball” L4DC 2021 Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics NeurIPS 2021 Learning to Predict Vehicle Trajectories with Model-based Planning CoRL 2021 Model-Based Offline Planning ICLR 2021 Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning AISTATS 2021 On the role of planning in model-based deep reinforcement learning ICLR 2021 Self-Consistent Models and Values NeurIPS 2021 Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains NeurIPS 2021 Bayesian Reinforcement Learning via Deep, Sparse Sampling AISTATS 2020 Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments CoRL 2020 On the Expressivity of Neural Networks for Deep Reinforcement Learning ICML 2020 Plan2Vec: Unsupervised Representation Learning by Latent Plans L4DC 2020 Planning and Execution using Inaccurate Models with Provable Guarantees RSS 2020 Planning from Images with Deep Latent Gaussian Process Dynamics L4DC 2020 Calibrated Model-Based Deep Reinforcement Learning ICML 2019 Entity Abstraction in Visual Model-Based Reinforcement Learning CoRL 2019 IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs AAAI 2019