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 9201–9225 of 15113 papers

TitleStatusHype
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Smoothing Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios—0
Train a snake with reinforcement learning algorithms—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement LearningCode0
Optimal Portfolio Liquidation—0
Virtual Autonomous Driving with Reinforcement Learning—0
Reinforcement Learning in 20Q Game with Generic Knowledge Bases—0
Mobile Robots Autonomous Exploration with Reinforcement Learning—0
Towards Understanding Deep Policy Gradients: A Case Study on PPO—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Optimization of Multi-Factor Model in Quantitative Trading Based On Reinforcement Learning—0
Reinforcement Learning for Contact-Rich Tasks: Robotic Peg Insertion StrategiesCode1
Ranking Items in Large-Scale Item Search Engines with Reinforcement Learning—0
Mobile Robots Exploration via Deep Reinforcement Learning—0
Reinforcement Learning Based Adaptive WalkingAssistance Control of a Lower Limb Exoskeleton—0
Reinforcement Learning for Predict+Optimize—0
Portfolio Management with Reinforcement Learning—0
Reinforcement Learning Based Character Controlling—0
Increasing Data Efficiency of Driving Agent By World ModelCode0
Cloud Database Tuning with Reinforcement LearningCode0
Learn to Play Tetris with Deep Reinforcement Learning—0
Evading Web Application Firewalls with Reinforcement Learning—0
IPM Move Planner: AN EFFICIENT EXPLOITING DEEP REINFORCEMENT LEARNING WITH MONTE CARLO TREE SEARCH—0
Learn To Manage Portfolio With Reinforcement Learning—0
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Benchmark Results

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