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

Showing 22012250 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
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
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
Compile Scene Graphs with Reinforcement LearningCode1
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing ProblemsCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Dream and Search to Control: Latent Space Planning for Continuous ControlCode1
DreamShard: Generalizable Embedding Table Placement for Recommender SystemsCode1
Dream to Control: Learning Behaviors by Latent ImaginationCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learningCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban DrivingCode1
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
Bridging RL Theory and Practice with the Effective HorizonCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous ControlCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
A Crash Course on Reinforcement LearningCode1
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
Reinforcement Learning in High-frequency Market MakingCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Concise Reasoning via Reinforcement LearningCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Efficient Wasserstein Natural Gradients for Reinforcement LearningCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
Evolutionary Planning in Latent SpaceCode1
Improved Representation of Asymmetrical Distances with Interval Quasimetric EmbeddingsCode1
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Benchmark Results

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