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 2861–2870 of 15113 papers

TitleStatusHype
Solving Bayesian inverse problems with diffusion priors and off-policy RL—0
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning—0
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach—0
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models—0
Zero-Shot Action Generalization with Limited Observations—0
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents—0
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model—0
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Benchmark Results

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