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 271–280 of 15113 papers

TitleStatusHype
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement LearningCode2
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
OpenRL: A Unified Reinforcement Learning FrameworkCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
Learning to Fly in SecondsCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
TD-MPC2: Scalable, Robust World Models for Continuous ControlCode2
Distributional Soft Actor-Critic with Three RefinementsCode2
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Benchmark Results

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