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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10251–10275 of 15113 papers

TitleStatusHype
Local Search for Policy Iteration in Continuous Control—0
Human-centric Dialog Training via Offline Reinforcement Learning—0
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control—0
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?—0
Smaller World Models for Reinforcement Learning—0
Remote Electrical Tilt Optimization via Safe Reinforcement Learning—0
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption—0
Nearly Minimax Optimal Reward-free Reinforcement Learning—0
Safe Reinforcement Learning with Natural Language Constraints—0
Contrastive Explanations for Reinforcement Learning via Embedded Self PredictionsCode0
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks—0
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems—0
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection—0
Trust the Model When It Is Confident: Masked Model-based Actor-Critic—0
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model UncertaintyCode0
Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning—0
Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning—0
Information-Driven Adaptive Sensing Based on Deep Reinforcement LearningCode0
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Benchmark Results

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