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 961–970 of 15113 papers

TitleStatusHype
Constrained Policy Optimization via Bayesian World ModelsCode1
Constructions in combinatorics via neural networksCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
How Can LLM Guide RL? A Value-Based ApproachCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Human-Level Control through Directly-Trained Deep Spiking Q-NetworksCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
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Benchmark Results

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