Methods

MAVEN: Multi-Agent Variational Exploration NeurIPS 2019 Maximum Entropy Monte-Carlo Planning NeurIPS 2019 Maximum Entropy-Regularized Multi-Goal Reinforcement Learning ICML 2019 Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards NeurIPS 2019 MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies NeurIPS 2019 Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning AAAI 2019 Meta-Inverse Reinforcement Learning with Probabilistic Context Variables NeurIPS 2019 Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset CVPR 2019 Meta Learning for Image Captioning AAAI 2019 Meta Reinforcement Learning with Task Embedding and Shared Policy IJCAI 2019 Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control IJCAI 2019 Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling ACL 2019 Model-Based Active Exploration ICML 2019 Model-based Behavioral Cloning with Future Image Similarity Learning CoRL 2019 Model-Based Planning with Energy-Based Models CoRL 2019 Model-Based Reinforcement Learning Exploiting State-Action Equivalence ACML 2019 Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches COLT 2019 Model-Free IRL Using Maximum Likelihood Estimation AAAI 2019 Model-Free Linear Quadratic Control via Reduction to Expert Prediction AISTATS 2019 Model-Free Model Reconciliation IJCAI 2019 Model Learning for Look-Ahead Exploration in Continuous Control AAAI 2019 Monte Carlo Tree Search for Policy Optimization IJCAI 2019 More Efficient Off-Policy Evaluation through Regularized Targeted Learning ICML 2019 Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation NeurIPS 2019 Multi-Agent Adversarial Inverse Reinforcement Learning ICML 2019