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 741–750 of 15113 papers

TitleStatusHype
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis—0
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning—0
LogLLaMA: Transformer-based log anomaly detection with LLaMA—0
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities—0
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning—0
Neural Lyapunov Function Approximation with Self-Supervised Reinforcement LearningCode0
Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor controlCode2
Cosmos-Reason1: From Physical Common Sense To Embodied ReasoningCode4
CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration—0
Pauli Network Circuit Synthesis with Reinforcement Learning—0
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Benchmark Results

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