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 2521–2530 of 15113 papers

TitleStatusHype
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants—0
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
Learning safety critics via a non-contractive binary bellman operator—0
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Active Inference as a Model of Agency—0
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning—0
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN—0
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Benchmark Results

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