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

Showing 16811690 of 15113 papers

TitleStatusHype
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Neural Laplace Control for Continuous-time Delayed SystemsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic RegularizationCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing TasksCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
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Benchmark Results

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