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 321–330 of 15113 papers

TitleStatusHype
DiffMimic: Efficient Motion Mimicking with Differentiable PhysicsCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
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Benchmark Results

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