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 7776–7800 of 15113 papers

TitleStatusHype
Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning—0
Model-predictive control and reinforcement learning in multi-energy system case studies—0
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming—0
Model Predictive Control via On-Policy Imitation Learning—0
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties—0
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
Model Selection in Reinforcement Learning with General Function Approximations—0
Model Selection for Generic Reinforcement Learning—0
Modified Actor-Critics—0
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks—0
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization—0
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
Modularity benefits reinforcement learning agents with competing homeostatic drives—0
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment—0
Modulated Policy Hierarchies—0
Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning—0
Molecular Generative Adversarial Network with Multi-Property Optimization—0
Mollification Effects of Policy Gradient Methods—0
Momentum in Reinforcement Learning—0
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking—0
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning—0
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Benchmark Results

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