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 3251–3260 of 15113 papers

TitleStatusHype
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs—0
Data-Efficient Reinforcement Learning in Continuous-State POMDPs—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Creativity in Robot Manipulation with Deep Reinforcement Learning—0
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach—0
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
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Benchmark Results

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