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 9876–9900 of 15113 papers

TitleStatusHype
GRAC: Self-Guided and Self-Regularized Actor-CriticCode0
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal MemoryCode0
A Contraction Approach to Model-based Reinforcement Learning—0
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation—0
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement LearningCode1
Reward Maximisation through Discrete Active Inference—0
Reconstructing Actions To Explain Deep Reinforcement Learning—0
Time your hedge with Deep Reinforcement Learning—0
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning—0
Transfer Learning in Deep Reinforcement Learning: A Survey—0
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
Text Generation by Learning from DemonstrationsCode1
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing—0
Reinforcement Learning for Strategic Recommendations—0
Soft policy optimization using dual-track advantage estimator—0
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Decoding Polar Codes with Reinforcement Learning—0
Decoupling Representation Learning from Reinforcement LearningCode2
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Efficient Transformers: A Survey—0
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
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Benchmark Results

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