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 2731–2740 of 15113 papers

TitleStatusHype
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and ChallengesCode0
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems—0
Fast Adaptation with Behavioral Foundation Models—0
Boosting Universal LLM Reward Design through the Heuristic Reward Observation Space Evolution—0
RL-based Control of UAS Subject to Significant Disturbance—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Better Decisions through the Right Causal World Model—0
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems—0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models—0
Trust-Region Twisted Policy ImprovementCode0
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Benchmark Results

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