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Policy Learning

2068 directly classified papers

Papers per year

Papers

Eventual Discounting Temporal Logic Counterfactual Experience Replay ICML 2023
AutoCost: Evolving Intrinsic Cost for Zero-Violation Reinforcement Learning AAAI 2023
Harnessing the Plug-and-Play Controller by Prompting EMNLP 2023
Policy Gradient in Robust MDPs with Global Convergence Guarantee ICML 2023
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis EMNLP 2023
Robust Average-Reward Markov Decision Processes AAAI 2023
trlX: A Framework for Large Scale Open Source RLHF EMNLP 2023
Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits ACML 2023
Continuous Versatile Jumping Using Learned Action Residuals L4DC 2023
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning ICML 2023
A Pragmatic Look at Deep Imitation Learning ACML 2023
Policy Evaluation in Distributional LQR L4DC 2023
Model Predictive Control via On-Policy Imitation Learning L4DC 2023
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning ICML 2023
Task-Oriented Koopman-Based Control with Contrastive Encoder CORL 2023
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning IJCAI 2023
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators ICML 2023
Reader: Model-based language-instructed reinforcement learning EMNLP 2023
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format EMNLP 2023
VA-learning as a more efficient alternative to Q-learning ICML 2023
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning EMNLP 2023
Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback EMNLP 2023
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction EMNLP 2023
Reinforced Target-driven Conversational Promotion EMNLP 2023
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning EMNLP 2023