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 2621–2630 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
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
USPR: Learning a Unified Solver for Profiled RoutingCode0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search—0
Multi-agent Embodied AI: Advances and Future Directions—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