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 61516200 of 15113 papers

TitleStatusHype
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning0
Learning to Select the Next Reasonable Mention for Entity Linking0
Learning to Shape Rewards using a Game of Two Partners0
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning0
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control0
Learning to solve arithmetic problems with a virtual abacus0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach0
Learning to Optimise Climate Sensor Placement using a Transformer0
Learning to Solve Combinatorial Problems via Efficient Exploration0
Learning to superoptimize programs - Workshop Version0
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes0
Learning to Teach in Cooperative Multiagent Reinforcement Learning0
Learning to Teach Reinforcement Learning Agents0
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning0
Learning to Transfer Role Assignment Across Team Sizes0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks0
Learning to Unknot0
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping0
Learning to View: Decision Transformers for Active Object Detection0
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation0
Learning to Walk via Deep Reinforcement Learning0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning0
Learning Transition Models with Time-delayed Causal Relations0
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning0
Reinforcement Learning for Mean Field Games with Strategic Complementarities0
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning0
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective0
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following0
Learning user-defined sub-goals using memory editing in reinforcement learning0
Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing0
Learning Value of Information towards Joint Communication and Control in 6G V2X0
Learning values across many orders of magnitude0
Learning Vehicle Routing Problems using Policy Optimisation0
Learning Vine Copula Models For Synthetic Data Generation0
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations0
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions0
Learning What to Memorize: Using Intrinsic Motivation to Form Useful Memory in Partially Observable Reinforcement Learning0
Learning what to read: Focused machine reading0
Learning When and What to Ask: a Hierarchical Reinforcement Learning Framework0
Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control0
Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator0
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning0
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs0
Learning with Social Influence through Interior Policy Differentiation0
Learning World Graph Decompositions To Accelerate Reinforcement Learning0
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning0
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent0
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Benchmark Results

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