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 2341–2350 of 15113 papers

TitleStatusHype
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes—0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
RL-Guided MPC for Autonomous Greenhouse Control—0
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps—0
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making—0
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under UncertaintyCode0
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document RetrievalCode0
ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification—0
Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning—0
Eliciting Reasoning in Language Models with Cognitive Tools—0
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Benchmark Results

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