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 471–480 of 15113 papers

TitleStatusHype
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback—0
LineFlow: A Framework to Learn Active Control of Production LinesCode0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs—0
On Corruption-Robustness in Performative Reinforcement Learning—0
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Benchmark Results

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