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 3651–3660 of 15113 papers

TitleStatusHype
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
CoRL: Environment Creation and Management Focused on System IntegrationCode1
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Learning to Influence Human Behavior with Offline Reinforcement Learning—0
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
POPGym: Benchmarking Partially Observable Reinforcement LearningCode2
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
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Benchmark Results

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