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 2651–2660 of 15113 papers

TitleStatusHype
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
The Effective Horizon Explains Deep RL Performance in Stochastic EnvironmentsCode1
An Invitation to Deep Reinforcement Learning—0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency—0
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL ApproachCode1
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
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Benchmark Results

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