Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation
3D generation has rapidly accelerated in the past decade owing to the progress in the field of generative modeling. Score Distillation Sampling (SDS) based rendering has improved 3D asset generation to a great extent. Fu…
3D GenerationDenoisingPolicy Gradient MethodsText to 3DScore-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems
In this paper, we introduce a policy-gradient method for model-based reinforcement learning (RL) that exploits a type of stationary distributions commonly obtained from Markov decision processes (MDPs) in stochastic netw…
FormModel-based Reinforcement LearningPolicy Gradient MethodsReinforcement Learning (RL)Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e.g. policy entropy regularisation) to randomise their actions in favor of exploration. This often …
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)A Large Deviations Perspective on Policy Gradient Algorithms
Motivated by policy gradient methods in the context of reinforcement learning, we identify a large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives sati…
Policy Gradient Methodsreinforcement-learningReinforcement LearningClipped-Objective Policy Gradients for Pessimistic Policy Optimization
To facilitate efficient learning, policy gradient approaches to deep reinforcement learning (RL) are typically paired with variance reduction measures and strategies for making large but safe policy changes based on a ba…
Deep Reinforcement LearningMulti-Task LearningPolicy Gradient MethodsReinforcement Learning (RL)On the Second-Order Convergence of Biased Policy Gradient Algorithms
Since the objective functions of reinforcement learning problems are typically highly nonconvex, it is desirable that policy gradient, the most popular algorithm, escapes saddle points and arrives at second-order station…
Policy Gradient MethodsRiemannian stochastic optimization methods avoid strict saddle points
Many modern machine learning applications - from online principal component analysis to covariance matrix identification and dictionary learning - can be formulated as minimization problems on Riemannian manifolds, and a…
Dictionary LearningPolicy Gradient MethodsRiemannian optimizationStochastic OptimizationFederated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a multi-task setting, in which each agent has …
Decision MakingPolicy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback
In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-fee…
Policy Gradient MethodsAccelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
Various acceleration approaches for Policy Gradient (PG) have been analyzed within the realm of Reinforcement Learning (RL). However, the theoretical understanding of the widely used momentum-based acceleration method on…
Policy Gradient Methodsreinforcement-learningReinforcement Learning (RL)$f$-Policy Gradients: A General Framework for Goal Conditioned RL using $f$-Divergences
Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy optimization a difficult problem. Sever…
Efficient ExplorationPolicy Gradient MethodsReinforcement Learning (RL)Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems. In those methods a deep neural network is used as a solution generator which is…
Policy Gradient MethodsTraveling Salesman ProblemGlobal Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control
Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential decision making in reinforcement learning…
Decision MakingPolicy Gradient Methodsreinforcement-learningSequential Decision MakingBeyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Markov Decision Processes (MDPs) are a formal framework for modeling and solving sequential decision-making problems. In finite-time horizons such problems are relevant for instance for optimal stopping or specific suppl…
Decision MakingPolicy Gradient MethodsSequential Decision MakingSample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to …
Policy Gradient MethodsReinforcement Learning (RL)Oracle Complexity Reduction for Model-free LQR: A Stochastic Variance-Reduced Policy Gradient Approach
We investigate the problem of learning an $\epsilon$-approximate solution for the discrete-time Linear Quadratic Regulator (LQR) problem via a Stochastic Variance-Reduced Policy Gradient (SVRPG) approach. Whilst policy g…
Policy Gradient MethodsLearning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence
Zero-sum Linear Quadratic (LQ) games are fundamental in optimal control and can be used (i)~as a dynamic game formulation for risk-sensitive or robust control and (ii)~as a benchmark setting for multi-agent reinforcement…
Multi-agent Reinforcement LearningPolicy Gradient MethodsCommodities Trading through Deep Policy Gradient Methods
Algorithmic trading has gained attention due to its potential for generating superior returns. This paper investigates the effectiveness of deep reinforcement learning (DRL) methods in algorithmic commodities trading. It…
Algorithmic TradingDeep Reinforcement LearningPolicy Gradient MethodsTime SeriesHindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning
Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme case, long trajectories of behavior are …
Decision MakingDeep Reinforcement LearningOff-policy evaluationPolicy Gradient Methods+3Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a promising paradigm for training control pol…
Policy Gradient Methods