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
RamseyRL: A Framework for Intelligent Ramsey Number Counterexample SearchingCode0
Language Reward Modulation for Pretraining Reinforcement LearningCode1
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems—0
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient QueryingCode0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Stabilizing Unsupervised Environment Design with a Learned Adversary—0
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL—0
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling—0
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning—0
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects—0
Reinforced Self-Training (ReST) for Language Modeling—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute NodesCode0
On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing—0
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World—0
ACRE: Actor-Critic with Reward-Preserving ExplorationCode0
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse—0
Omega-Regular Reward Machines—0
Insurance pricing on price comparison websites via reinforcement learning—0
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads—0
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Benchmark Results

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