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 4531–4540 of 15113 papers

TitleStatusHype
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Low-Rank MDPs with Continuous Action Spaces—0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets—0
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models—0
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario—0
Domain Randomization via Entropy Maximization—0
Imitation Bootstrapped Reinforcement Learning—0
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Benchmark Results

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