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 1311–1320 of 15113 papers

TitleStatusHype
Plasticity Loss in Deep Reinforcement Learning: A Survey—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting DiversityCode0
Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations—0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping—0
Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination GamesCode0
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
Opportunities of Reinforcement Learning in South Africa's Just Transition—0
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Benchmark Results

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