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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10051–10075 of 15113 papers

TitleStatusHype
Multi-Reward based Reinforcement Learning for Neural Machine Translation—0
Multi-Robot Active Mapping via Neural Bipartite Graph Matching—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning—0
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots—0
Multi-Robot Formation Control Using Reinforcement Learning—0
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
Multi-step Greedy Reinforcement Learning Algorithms—0
Multi-step Reinforcement Learning: A Unifying Algorithm—0
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions—0
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior—0
Multi-Task Federated Reinforcement Learning with Adversaries—0
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems—0
Multi-task Learning for Continuous Control—0
Multi-task learning with deep model based reinforcement learning—0
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks—0
Multi-Task Policy Search—0
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping—0
Multi-Task Reinforcement Learning for Quadrotors—0
Multi-task Reinforcement Learning with a Planning Quasi-Metric—0
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Benchmark Results

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