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 3426–3450 of 15113 papers

TitleStatusHype
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
Comparison of Reinforcement Learning algorithms applied to the Cart Pole problemCode0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
Guided Exploration in Reinforcement Learning via Monte Carlo Critic OptimizationCode0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
Guided Deep Reinforcement Learning for Swarm SystemsCode0
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented DialogCode0
Guide Actor-Critic for Continuous ControlCode0
Adversarial Learning for Neural Dialogue GenerationCode0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Deep Learning in Neural Networks: An OverviewCode0
A User Simulator for Task-Completion DialoguesCode0
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
A Regularized Opponent Model with Maximum Entropy ObjectiveCode0
Group-driven Reinforcement Learning for Personalized mHealth InterventionCode0
Group Equivariant Deep Reinforcement LearningCode0
Growing Action SpacesCode0
Deep Reinforcement Learning with Modulated Hebbian plus Q Network ArchitectureCode0
Grounding Language for Transfer in Deep Reinforcement LearningCode0
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code GenerationCode0
Adversarial Intrinsic Motivation for Reinforcement LearningCode0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and ControlCode0
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Benchmark Results

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