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 1901–1910 of 15113 papers

TitleStatusHype
Constructing Ancestral Recombination Graphs through Reinforcement Learning—0
Adding Conditional Control to Diffusion Models with Reinforcement Learning—0
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms—0
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
UniZero: Generalized and Efficient Planning with Scalable Latent World Models—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
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Benchmark Results

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