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 5451–5475 of 15113 papers

TitleStatusHype
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with TransformerCode1
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization—0
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning—0
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games—0
Sim2real for Reinforcement Learning Driven Next Generation Networks—0
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning—0
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation GuidelinesCode0
A Study of Continual Learning Methods for Q-Learning—0
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance—0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Imitating Past Successes can be Very Suboptimal—0
Learning in Observable POMDPs, without Computationally Intractable Oracles—0
A Model-Based Reinforcement Learning Approach for PID Design—0
Discrete State-Action Abstraction via the Successor RepresentationCode0
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
Driving in Real Life with Inverse Reinforcement Learning—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
On the Role of Discount Factor in Offline Reinforcement Learning—0
Introspective Experience Replay: Look Back When SurprisedCode0
MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN—0
Variational Meta Reinforcement Learning for Social Robotics—0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
Learning Generalized Wireless MAC Communication Protocols via Abstraction—0
Efficient entity-based reinforcement learning—0
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Benchmark Results

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