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Policy Gradient Methods

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Showing 176–200 of 382 papers

TitleStatusHype
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Variance Reduced Domain Randomization for Policy Gradient—0
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization—0
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
Variance Reduction in Actor Critic Methods (ACM)—0
When Do Off-Policy and On-Policy Policy Gradient Methods Align?—0
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies—0
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization—0
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning—0
Federated Reinforcement Learning with Constraint Heterogeneity—0
Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Fingerprint Policy Optimisation for Robust Reinforcement Learning—0
Focused Hierarchical RNNs for Conditional Sequence Processing—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
From Imitation to Refinement -- Residual RL for Precise Assembly—0
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction—0
Geometry and convergence of natural policy gradient methods—0
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries—0
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction—0
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator—0
Global Convergence of Policy Gradient Methods for Linearized Control Problems—0
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies—0
Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control—0
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