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 3531–3540 of 15113 papers

TitleStatusHype
On-line reinforcement learning for optimization of real-life energy trading strategy—0
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Planning with Sequence Models through Iterative Energy Minimization—0
Robust Risk-Aware Option Hedging—0
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning—0
Inverse Reinforcement Learning without Reinforcement LearningCode1
Control of synaptic plasticity via the fusion of reinforcement learning and unsupervised learning in neural networks—0
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic EnvironmentsCode0
marl-jax: Multi-Agent Reinforcement Leaning FrameworkCode1
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Benchmark Results

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