Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Variance Reduced Domain Randomization for Policy Gradient
By introducing randomness on environment parameters that fundamentally affect the dynamics, domain randomization (DR) imposes diversity to the policy trained by deep reinforcement learning, and thus improves its capabili…
Deep Reinforcement LearningPolicy Gradient MethodsAsynchronous Multi-Agent Actor-Critic with Macro-Actions
Many realistic multi-agent problems naturally require agents to be capable of performing asynchronously without waiting for other agents to terminate (e.g., multi-robot domains). Such problems can be modeled as Macro-Act…
Decision MakingPolicy Gradient MethodsSample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games
We introduce algorithms based on natural policy gradient and two time-scale natural actor-critic, and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case. Our results improve …
Policy Gradient MethodsEfficient Wasserstein and Sinkhorn Policy Optimization
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we examine two natural extensions of policy optimziation with Wass…
Policy Gradient MethodsReinforcement Learning (RL)Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience. Yet, policy gradient methods suffer convergence problems to a Nash Equi…
counterfactualDeep Reinforcement LearningPolicy Gradient MethodsEvolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning
This paper investigates the performance of Scalable Evolution Strategies (S-ES) as a Hierarchical Reinforcement Learning (HRL) approach. S-ES, named for its excellent scalability across many processors, was popularised b…
Hierarchical Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
When designing algorithms for finite-time-horizon episodic reinforcement learning problems, a common approach is to introduce a fictitious discount factor and use stationary policies for approximations. Empirically, it h…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Opinion Summarizers by Selecting Informative Reviews
Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques. In this work, we collect a large dataset of summaries paired with user reviews for over 31,00…
Few-Shot LearningOpinion SummarizationPolicy Gradient MethodsVariational InferenceA general class of surrogate functions for stable and efficient reinforcement learning
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong theoretical guarantees, leading to algo…
MuJoCoPolicy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estim…
continuous-controlContinuous ControlData AugmentationDeep Reinforcement Learning+5Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium. In order to prove th…
Policy Gradient MethodsHindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
Policy gradient methods are appealing in deep reinforcement learning but suffer from high variance of gradient estimate. To reduce the variance, the state value function is applied commonly. However, the effect of the st…
Deep Reinforcement LearningPolicy Gradient MethodsProximal Policy Optimization for Tracking Control Exploiting Future Reference Information
In recent years, reinforcement learning (RL) has gained increasing attention in control engineering. Especially, policy gradient methods are widely used. In this work, we improve the tracking performance of proximal poli…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification. Many different approaches have been explored for approximate policy evaluation, but less i…
Policy Gradient MethodsFine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. H…
ClassificationData AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Policy Gradient Methods for Distortion Risk Measures
We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an epis…
Policy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Curious Explorer: a provable exploration strategy in Policy Learning
Having access to an exploring restart distribution (the so-called wide coverage assumption) is critical with policy gradient methods. This is due to the fact that, while the objective function is insensitive to updates i…
Policy Gradient MethodsModularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Many transfer problems require re-using previously optimal decisions for solving new tasks, which suggests the need for learning algorithms that can modify the mechanisms for choosing certain actions independently of tho…
Decision MakingPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1End-to-End Neuro-Symbolic Architecture for Image-to-Image Reasoning Tasks
Neural models and symbolic algorithms have recently been combined for tasks requiring both perception and reasoning. Neural models ground perceptual input into a conceptual vocabulary, on which a classical reasoning algo…
Image ReconstructionPolicy Gradient MethodsAd Headline Generation using Self-Critical Masked Language Model
For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a pro…
Headline GenerationLanguage ModelingLanguage ModellingPolicy Gradient Methods+1