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 971–980 of 15113 papers

TitleStatusHype
Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUsCode2
Flow Q-LearningCode3
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
Circular Microalgae-Based Carbon Control for Net ZeroCode0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Analytical Lyapunov Function Discovery: An RL-based Generative ApproachCode1
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
Process Reinforcement through Implicit RewardsCode5
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
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Benchmark Results

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