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 491–500 of 15113 papers

TitleStatusHype
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
DeepMind Lab2DCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
Continuous-Time Model-Based Reinforcement LearningCode1
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Benchmark Results

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