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 4371–4380 of 15113 papers

TitleStatusHype
Adversarial Text Generation Without Reinforcement Learning—0
Adversarial Training Blocks Generalization in Neural Policies—0
Adversary A3C for Robust Reinforcement Learning—0
Adversary agent reinforcement learning for pursuit-evasion—0
Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop—0
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
A dynamic game approach to training robust deep policies—0
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn—0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
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Benchmark Results

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