Policy Learning

A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems AAAI 2020 A Multi-Objective Approach to Mitigate Negative Side Effects IJCAI 2020 An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search NeurIPS 2020 An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods NeurIPS 2020 An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies AAAI 2020 A Nonparametric Off-Policy Policy Gradient AISTATS 2020 An operator view of policy gradient methods NeurIPS 2020 A Self-Tuning Actor-Critic Algorithm NeurIPS 2020 Asynchronous Coagent Networks ICML 2020 Attentive Experience Replay AAAI 2020 Avoiding Side Effects By Considering Future Tasks NeurIPS 2020 BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning NeurIPS 2020 Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration AISTATS 2020 Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy AAAI 2020 Boundary Extension Features for Width-Based Planning with Simulators on Continuous-State Domains IJCAI 2020 BRPO: Batch Residual Policy Optimization IJCAI 2020 Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning? CVPR 2020 Causal Imitation Learning With Unobserved Confounders NeurIPS 2020 Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies ICML 2020 Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue COLING 2020 Combining Q-Learning and Search with Amortized Value Estimates ICLR 2020 Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations RSS 2020 Conservative Exploration in Reinforcement Learning AISTATS 2020 Constrained Markov Decision Processes via Backward Value Functions ICML 2020 Constrained Policy Improvement for Efficient Reinforcement Learning IJCAI 2020