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 6951–6975 of 15113 papers

TitleStatusHype
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation—0
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization—0
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning—0
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems—0
Alternating Good-for-MDP Automata—0
Reinforcement Learning Approach to Estimation in Linear Systems—0
Rapid Locomotion via Reinforcement Learning—0
Multi-Agent Deep Reinforcement Learning in Vehicular OCC—0
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning—0
General sum stochastic games with networked information flows—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
Generative methods for sampling transition paths in molecular dynamics—0
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
Exploring the Benefits of Teams in Multiagent Learning—0
State Representation Learning for Goal-Conditioned Reinforcement Learning—0
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions—0
Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures—0
Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for Cognitive Radars—0
RLFlow: Optimising Neural Network Subgraph Transformation with World ModelsCode0
Triangular Dropout: Variable Network Width without Retraining—0
Deep-Attack over the Deep Reinforcement Learning—0
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Benchmark Results

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