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

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Showing 9851–9875 of 15113 papers

TitleStatusHype
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Smoothing Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios—0
Optimal Portfolio Liquidation—0
Ranking Items in Large-Scale Item Search Engines with Reinforcement Learning—0
Mobile Robots Autonomous Exploration with Reinforcement Learning—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Portfolio Management with Reinforcement Learning—0
Virtual Autonomous Driving with Reinforcement Learning—0
Reinforcement Learning Based Adaptive WalkingAssistance Control of a Lower Limb Exoskeleton—0
Reinforcement Learning Based Character Controlling—0
Reinforcement Learning for Predict+Optimize—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
Towards Understanding Deep Policy Gradients: A Case Study on PPO—0
Optimization of Multi-Factor Model in Quantitative Trading Based On Reinforcement Learning—0
Mobile Robots Exploration via Deep Reinforcement Learning—0
Reinforcement Learning in 20Q Game with Generic Knowledge Bases—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Evolutionary learning of interpretable decision treesCode0
Cloud Database Tuning with Reinforcement LearningCode0
Deploying Reinforcement Learning in Water Transport—0
Evading Web Application Firewalls with Reinforcement Learning—0
Demystify Painting with RL—0
Active Hierarchical Imitation and Reinforcement Learning—0
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability—0
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL—0
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Benchmark Results

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