Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents. …
Policy Gradient MethodsReinforcement Learning (RL)Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible metrics and examine two natural extension…
continuous-controlContinuous ControlPolicy Gradient MethodsCorrecting discount-factor mismatch in on-policy policy gradient methods
The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{…
OpenAI GymPolicy Gradient MethodsNeural Inventory Control in Networks via Hindsight Differentiable Policy Optimization
We argue that inventory management presents unique opportunities for reliably applying and evaluating deep reinforcement learning (DRL). Toward reliable application, we emphasize and test two techniques. The first is Hin…
Deep Reinforcement LearningManagementPolicy Gradient MethodsReinforcement Learning (RL)+1Acceleration in Policy Optimization
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. Leveraging the …
Meta-LearningPolicy Gradient MethodsReinforcement Learning (RL)Deep Policy Gradient Methods in Commodity Markets
The energy transition has increased the reliance on intermittent energy sources, destabilizing energy markets and causing unprecedented volatility, culminating in the global energy crisis of 2021. In addition to harming …
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement LearningStepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes
Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces. Unfortunately, these methods require p…
Meta Reinforcement LearningPolicy Gradient MethodsConfidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exp…
Policy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Robot NavigationEfficient Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performanc…
D4RLOffline RLPolicy Gradient Methodsreinforcement-learning+2Solving Robust MDPs through No-Regret Dynamics
Reinforcement Learning is a powerful framework for training agents to navigate different situations, but it is susceptible to changes in environmental dynamics. However, solving Markov Decision Processes that are robust …
NavigatePolicy Gradient MethodsAdaptive Policy Learning to Additional Tasks
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which…
Policy Gradient MethodsShattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward model to emulate human feedback, distinct fr…
Efficient ExplorationLanguage ModelingLanguage ModellingPolicy Gradient Methods+2Client Selection for Federated Policy Optimization with Environment Heterogeneity
The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods that gained both theoretical soundness and empirical success on a va…
MuJoCoPolicy Gradient MethodsReinforcement Learning (RL)Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Reinforcement learning (RL) on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the continuous-control setting, and extend the…
continuous-controlContinuous ControlPolicy Gradient MethodsReinforcement Learning (RL)+1Online Portfolio Management via Deep Reinforcement Learning with High-Frequency Data
Recently, models that based on Transformer have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in th…
Deep Reinforcement LearningManagementPolicy Gradient MethodsPortfolio Optimization+2Policy Gradient Methods for Discrete Time Linear Quadratic Regulator With Random Parameters
This paper studies an infinite horizon optimal control problem for discrete-time linear system and quadratic criteria, both with random parameters which are independent and identically distributed with respect to time. I…
Policy Gradient Methodsreinforcement-learningPolicy Mirror Descent Inherently Explores Action Space
Explicit exploration in the action space was assumed to be indispensable for online policy gradient methods to avoid a drastic degradation in sample complexity, for solving general reinforcement learning problems over fi…
Efficient ExplorationGeneral Reinforcement LearningPolicy Gradient MethodsPolicy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing
We develop policy gradients methods for stochastic control with exit time in a model-free setting. We propose two types of algorithms for learning either directly the optimal policy or by learning alternately the value f…
Policy Gradient MethodsA Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee
We consider policy gradient methods for stochastic optimal control problem in continuous time. In particular, we analyze the gradient flow for the control, viewed as a continuous time limit of the policy gradient method.…
Policy Gradient MethodsStochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Recently, the impressive empirical success of policy gradient (PG) methods has catalyzed the development of their theoretical foundations. Despite the huge efforts directed at the design of efficient stochastic PG-type a…
Policy Gradient Methods