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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2841–2850 of 15113 papers

TitleStatusHype
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
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Benchmark Results

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