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 1011–1020 of 15113 papers

TitleStatusHype
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement LearningCode1
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning AlgorithmCode1
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Towards Sequence-Level Training for Visual TrackingCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
Robust Reinforcement Learning using Offline DataCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
Object Detection with Deep Reinforcement LearningCode1
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching AgentCode1
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Benchmark Results

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