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 1401–1450 of 15113 papers

TitleStatusHype
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
Goal-Conditioned Generators of Deep PoliciesCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real TransferCode1
Concise Reasoning via Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic EnvironmentsCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI AgentsCode1
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
ARLO: A Framework for Automated Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
Hierarchical Reinforcement Learning for Power Network Topology ControlCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Hindsight Preference Learning for Offline Preference-based Reinforcement LearningCode1
HIQL: Offline Goal-Conditioned RL with Latent States as ActionsCode1
Combining Modular Skills in Multitask LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Compile Scene Graphs with Reinforcement LearningCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
Hypernetworks in Meta-Reinforcement LearningCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog AgentsCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
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Benchmark Results

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