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 4876–4900 of 15113 papers

TitleStatusHype
Can Offline Reinforcement Learning Help Natural Language Understanding?—0
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture—0
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks—0
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)—0
On the Reuse Bias in Off-Policy Reinforcement LearningCode0
ProAPT: Projection of APT Threats with Deep Reinforcement Learning—0
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control—0
Analysis of Reinforcement Learning for determining task replication in workflows—0
Feature-Rich Long-term Bitcoin Trading Assistant—0
Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence FunctionCode0
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation—0
Robust Constrained Reinforcement Learning—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
A new Reinforcement Learning framework to discover natural flavor molecules—0
Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
Model-based Reinforcement Learning with Multi-step Plan Value EstimationCode1
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
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Benchmark Results

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