Reinforcement Learning

Online Planning with Lookahead Policies NeurIPS 2020 Only Relevant Information Matters: Filtering Out Noisy Samples To Boost RL IJCAI 2020 On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract) IJCAI 2020 On the Convergence of Model Free Learning in Mean Field Games AAAI 2020 On the design of consequential ranking algorithms UAI 2020 On the Role of Weight Sharing During Deep Option Learning AAAI 2020 Optimistic Policy Optimization with Bandit Feedback ICML 2020 Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning AISTATS 2020 Optimizing Data Usage via Differentiable Rewards ICML 2020 Options of Interest: Temporal Abstraction with Interest Functions AAAI 2020 PackIt: A Virtual Environment for Geometric Planning ICML 2020 Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning AAAI 2020 Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning AAAI 2020 Periodic Q-Learning L4DC 2020 Pitfalls of Learning a Reward Function Online IJCAI 2020 Planning with Abstract Learned Models While Learning Transferable Subtasks AAAI 2020 Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes AAAI 2020 Policy Search by Target Distribution Learning for Continuous Control AAAI 2020 Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning ICML 2020 POPCORN: Partially Observed Prediction Constrained Reinforcement Learning AISTATS 2020 Predictive Coding for Locally-Linear Control ICML 2020 Predictive Information Accelerates Learning in RL NeurIPS 2020 Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms EMNLP 2020 Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation ICML 2020 Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract) AAAI 2020