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 3741–3750 of 15113 papers

TitleStatusHype
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
Deep Reinforcement Learning for Tensegrity Robot Locomotion—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks—0
Discourse-Aware Neural Rewards for Coherent Text Generation—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning—0
Deep Reinforcement Learning for Trading—0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
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Benchmark Results

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