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 1451–1460 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning Using Advantage-Based InterventionCode1
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Deep Reinforcement Learning for Conservation DecisionsCode1
Randomized Exploration for Reinforcement Learning with General Value Function ApproximationCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
Learning Intrusion Prevention Policies through Optimal StoppingCode1
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
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Benchmark Results

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