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 10051–10100 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
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic—0
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options—0
Multi-timescale Nexting in a Reinforcement Learning Robot—0
Multi-timestep models for Model-based Reinforcement Learning—0
Multi-trainer Interactive Reinforcement Learning System—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning—0
Multi-User Reinforcement Learning with Low Rank Rewards—0
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing—0
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving—0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning—0
Mungojerrie: Reinforcement Learning of Linear-Time Objectives—0
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning—0
MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization—0
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning—0
MUST: A Framework for Training Task-oriented Dialogue Systems with Multiple User SimulaTors—0
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study—0
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling—0
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning—0
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning—0
Mutual Reinforcement Learning—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
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Benchmark Results

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