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 3401–3425 of 15113 papers

TitleStatusHype
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
Complex Model Transformations by Reinforcement Learning with Uncertain Human GuidanceCode0
A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute NodesCode0
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and GazeboCode0
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
Handling Delay in Real-Time Reinforcement LearningCode0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal DemonstrationsCode0
Guided Policy Optimization under Partial ObservabilityCode0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
Competing for pixels: a self-play algorithm for weakly-supervised segmentationCode0
Guided Exploration in Reinforcement Learning via Monte Carlo Critic OptimizationCode0
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented DialogCode0
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied AgentsCode0
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning ApproachCode0
Guide Actor-Critic for Continuous ControlCode0
Comparison of Reinforcement Learning algorithms applied to the Cart Pole problemCode0
Group Equivariant Deep Reinforcement LearningCode0
Growing Action SpacesCode0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
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Benchmark Results

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