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 3631–3640 of 15113 papers

TitleStatusHype
Advances in Preference-based Reinforcement Learning: A Review—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba—0
Enhancing Reinforcement Learning Through Guided Search—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
World Models Increase Autonomy in Reinforcement Learning—0
MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions—0
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning—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