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 2201–2225 of 15113 papers

TitleStatusHype
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Distributed Heuristic Multi-Agent Path Finding with CommunicationCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Reinforcement Learning in High-frequency Market MakingCode1
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Does Zero-Shot Reinforcement Learning Exist?Code1
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMsCode1
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement LearningCode1
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI EconomistCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Drafting in Collectible Card Games via Reinforcement LearningCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
DreamShard: Generalizable Embedding Table Placement for Recommender SystemsCode1
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous DrivingCode1
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route PredictionCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
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Benchmark Results

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