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Policy Gradient Methods

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Showing 251–300 of 382 papers

TitleStatusHype
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles—0
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks—0
Natural Policy Gradients In Reinforcement Learning Explained—0
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks—0
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence—0
Non-Parametric Stochastic Policy Gradient with Strategic Retreat for Non-Stationary Environment—0
Object Exchangeability in Reinforcement Learning: Extended Abstract—0
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline—0
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient—0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment—0
On the Convergence of Discounted Policy Gradient Methods—0
On the convergence of policy gradient methods to Nash equilibria in general stochastic games—0
On the Convergence Rates of Policy Gradient Methods—0
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures—0
On the Global Convergence Rates of Softmax Policy Gradient Methods—0
On the Linear convergence of Natural Policy Gradient Algorithm—0
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator—0
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift—0
Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient—0
Acceleration in Policy Optimization—0
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property—0
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result—0
Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback—0
Optimizing over a Restricted Policy Class in Markov Decision Processes—0
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods—0
Ordering-based Conditions for Global Convergence of Policy Gradient Methods—0
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation—0
PGPS : Coupling Policy Gradient with Population-based Search—0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Policy Gradient for Coherent Risk Measures—0
Policy Gradient for Rectangular Robust Markov Decision Processes—0
Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing—0
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games—0
Policy Gradient Methods for Designing Dynamic Output Feedback Controllers—0
Policy Gradient Methods for Discrete Time Linear Quadratic Regulator With Random Parameters—0
Policy Gradient Methods for Off-policy Control—0
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon—0
Policy Gradient Optimization of Thompson Sampling Policies—0
Policy Gradients for Contextual Recommendations—0
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo—0
Policy Mirror Descent Inherently Explores Action Space—0
Policy Optimization by Genetic Distillation—0
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence—0
Policy Optimization for H_2 Linear Control with H_ Robustness Guarantee: Implicit Regularization and Global Convergence—0
Policy Optimization with Demonstrations—0
Policy Optimization with Stochastic Mirror Descent—0
Policy Search by Target Distribution Learning for Continuous Control—0
Policy Search for Motor Primitives in Robotics—0
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