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 2331–2340 of 15113 papers

TitleStatusHype
Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques—0
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning—0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning—0
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
Noisy Spiking Actor Network for Exploration—0
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning—0
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global WorkspaceCode0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
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Benchmark Results

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