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 1681–1690 of 15113 papers

TitleStatusHype
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
Enhancing Reinforcement Learning Through Guided Search—0
MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions—0
World Models Increase Autonomy in Reinforcement Learning—0
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning—0
Reinforcement learning-based adaptive speed controllers in mixed autonomy condition—0
Vanilla Gradient Descent for Oblique Decision TreesCode0
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Benchmark Results

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