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 8026–8050 of 15113 papers

TitleStatusHype
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
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning—0
NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks—0
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator—0
Natural Gradient Deep Q-learning—0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning—0
Natural Language Person Search Using Deep Reinforcement Learning—0
Natural Language Reinforcement Learning—0
Language is Power: Representing States Using Natural Language in Reinforcement Learning—0
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning—0
Natural Policy Gradient for Average Reward Non-Stationary RL—0
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Benchmark Results

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