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 2921–2930 of 15113 papers

TitleStatusHype
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques—0
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Searching for High-Value Molecules Using Reinforcement Learning and Transformers—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
PGDQN: Preference-Guided Deep Q-NetworkCode1
Navigating Uncertainty in ESG Investing—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
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Benchmark Results

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