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Policy Learning

2068 directly classified papers

Papers per year

Papers

Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning IJCAI 2023
Revisiting Bellman Errors for Offline Model Selection ICML 2023
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees IJCAI 2023
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning ICML 2023
Posterior Sampling for Deep Reinforcement Learning ICML 2023
Hierarchical State Abstraction based on Structural Information Principles IJCAI 2023
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis EMNLP 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023
Beyond Reward: Offline Preference-guided Policy Optimization ICML 2023
Performative Reinforcement Learning ICML 2023
Practical Critic Gradient based Actor Critic for On-Policy Reinforcement Learning L4DC 2023
On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs ICML 2023
Adaptive Reward Shifting Based on Behavior Proximity for Offline Reinforcement Learning IJCAI 2023
Policy Gradient Play with Networked Agents in Markov Potential Games L4DC 2023
Policy Learning for Active Target Tracking over Continuous $SE(3)$ Trajectories L4DC 2023
Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching L4DC 2023
Robust Satisficing MDPs ICML 2023
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction EMNLP 2023
Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs L4DC 2023
Diversify Question Generation with Retrieval-Augmented Style Transfer EMNLP 2023
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators ICML 2023
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning ICML 2023
Exploiting Multiple Abstractions in Episodic RL via Reward Shaping AAAI 2023
Quantile Credit Assignment ICML 2023
Towards Theoretical Understanding of Inverse Reinforcement Learning ICML 2023