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 7826–7850 of 15113 papers

TitleStatusHype
MP3: Movement Primitive-Based (Re-)Planning Policy—0
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control—0
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles—0
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server—0
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty—0
MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration—0
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection—0
MSRL: Distributed Reinforcement Learning with Dataflow Fragments—0
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation—0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale—0
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion—0
Multi-Advisor Reinforcement Learning—0
Multi-Agent Actor-Critic with Generative Cooperative Policy Network—0
Multi-Agent Adversarial Attacks for Multi-Channel Communications—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning—0
Multi-agent Battery Storage Management using MPC-based Reinforcement Learning—0
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures—0
Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
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Benchmark Results

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