SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 4301–4310 of 15113 papers

TitleStatusHype
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions—0
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Ad Headline Generation using Self-Critical Masked Language Model—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation—0
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning—0
A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified