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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1511–1520 of 15113 papers

TitleStatusHype
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AICode1
Beyond Expected Returns: A Policy Gradient Algorithm for Cumulative Prospect Theoretic Reinforcement Learning—0
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability—0
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
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Benchmark Results

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