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 7201–7225 of 15113 papers

TitleStatusHype
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning—0
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework—0
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control—0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
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
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature ControlCode0
Random Ensemble Reinforcement Learning for Traffic Signal Control—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
Learning Torque Control for Quadrupedal Locomotion—0
Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3—0
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization—0
Gym-saturation: an OpenAI Gym environment for saturation provers—0
Investigation of Factorized Optical Flows as Mid-Level Representations—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
Robot Learning of Mobile Manipulation with Reachability Behavior Priors—0
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models—0
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks—0
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Benchmark Results

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