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 3051–3075 of 15113 papers

TitleStatusHype
A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Adapting Auxiliary Losses Using Gradient Similarity—0
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning—0
A Graph Attention Learning Approach to Antenna Tilt Optimization—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
A Survey on Model-based Reinforcement Learning—0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
AGPNet -- Autonomous Grading Policy Network—0
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning—0
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters—0
LTL-Constrained Policy Optimization with Cycle Experience Replay—0
A survey on intrinsic motivation in reinforcement learning—0
A Survey on Interpretable Reinforcement Learning—0
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback—0
A Survey on Dialog Management: Recent Advances and Challenges—0
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management—0
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
A Survey on Deep Reinforcement Learning for Audio-Based Applications—0
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning—0
Accidental exploration through value predictors—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
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Benchmark Results

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