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 4321–4330 of 15113 papers

TitleStatusHype
HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning ExperimentsCode0
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
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning—0
Learning safety critics via a non-contractive binary bellman operator—0
Active Inference as a Model of Agency—0
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants—0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN—0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
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Benchmark Results

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