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Policy Gradient Methods

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Showing 201–250 of 382 papers

TitleStatusHype
On the Second-Order Convergence of Biased Policy Gradient Algorithms—0
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains—0
Programmatic Reinforcement Learning without Oracles—0
Provable Policy Gradient Methods for Average-Reward Markov Potential Games—0
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods—0
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games—0
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information—0
Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy—0
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design—0
Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods—0
Residual Policy Gradient: A Reward View of KL-regularized Objective—0
Rethinking Deep Policy Gradients via State-Wise Policy Improvement—0
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate—0
Reward-estimation variance elimination in sequential decision processes—0
Riemannian stochastic optimization methods avoid strict saddle points—0
Risk-Sensitive Reinforcement Learning via Policy Gradient Search—0
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation—0
ROCM: RLHF on consistency models—0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games—0
Sample-efficient Deep Reinforcement Learning for Dialog Control—0
Sample Efficient Reinforcement Learning with REINFORCE—0
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL—0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
Self-Evolving Curriculum for LLM Reasoning—0
Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework—0
Self-Supervised Continuous Control without Policy Gradient—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models—0
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)—0
Softmax Policy Gradient Methods Can Take Exponential Time to Converge—0
SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search—0
SoftTreeMax: Policy Gradient with Tree Search—0
Solving Robust MDPs through No-Regret Dynamics—0
Solving Rubik's Cube Without Tricky Sampling—0
Solving Zero-Sum Convex Markov Games—0
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin—0
Stabilizing Dynamical Systems via Policy Gradient Methods—0
Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process—0
StartNet: Online Detection of Action Start in Untrimmed Videos—0
Statistically Efficient Off-Policy Policy Gradients—0
Stein Variational Policy Gradient—0
Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes—0
Stochastic Dimension-reduced Second-order Methods for Policy Optimization—0
Stochastic first-order methods for average-reward Markov decision processes—0
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies—0
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