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 331–340 of 15113 papers

TitleStatusHype
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
Meta-reinforcement learning with minimum attention—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software DevelopmentCode2
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning—0
GRIT: Teaching MLLMs to Think with Images—0
RLBenchNet: The Right Network for the Right Reinforcement Learning TaskCode1
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Benchmark Results

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