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

Showing 13111320 of 15113 papers

TitleStatusHype
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement LearningCode1
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulasCode1
Are Expressive Models Truly Necessary for Offline RL?Code1
Energy-Based Imitation LearningCode1
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
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Benchmark Results

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