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 3551–3575 of 15113 papers

TitleStatusHype
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids—0
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems—0
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery—0
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference—0
Deep Reinforcement Learning Boosted by External Knowledge—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
Depth and nonlinearity induce implicit exploration for RL—0
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR—0
Deep Reinforcement Learning Discovers Internal Models—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Derivative-Free Reinforcement Learning: A Review—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Dependency Parsing with Backtracking using Deep Reinforcement Learning—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
A statistical learning strategy for closed-loop control of fluid flows—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Deep Reinforcement Learning for Asset Allocation: Reward Clipping—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games—0
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Benchmark Results

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