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 3351–3375 of 15113 papers

TitleStatusHype
Active Object Localization with Deep Reinforcement LearningCode0
Handling Delay in Real-Time Reinforcement LearningCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment RegimesCode0
Compositional Learning of Visually-Grounded Concepts Using ReinforcementCode0
Deconfounding Reinforcement Learning in Observational SettingsCode0
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and GazeboCode0
Compositional Conservatism: A Transductive Approach in Offline Reinforcement LearningCode0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
Guided Exploration in Reinforcement Learning via Monte Carlo Critic OptimizationCode0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based roboticsCode0
Decoupling regularization from the action spaceCode0
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied AgentsCode0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
Neural Logic Reinforcement LearningCode0
Guided Policy Optimization under Partial ObservabilityCode0
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
Guided Deep Reinforcement Learning for Swarm SystemsCode0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
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Benchmark Results

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