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 10076–10100 of 15113 papers

TitleStatusHype
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