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 6026–6050 of 15113 papers

TitleStatusHype
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant—0
Orchestrated Value Mapping for Reinforcement LearningCode0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning AlgorithmsCode1
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems—0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism—0
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation—0
Random Ensemble Reinforcement Learning for Traffic Signal Control—0
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature ControlCode0
Learning Torque Control for Quadrupedal Locomotion—0
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization—0
Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement LearningCode0
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning—0
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language InferenceCode0
Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset tradingCode1
Gym-saturation: an OpenAI Gym environment for saturation provers—0
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Benchmark Results

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