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 3021–3030 of 15113 papers

TitleStatusHype
Circular Microalgae-Based Carbon Control for Net ZeroCode0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning—0
Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
Reinforcement Learning for Long-Horizon Interactive LLM Agents—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Reinforcement Learning with Segment Feedback—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
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Benchmark Results

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