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 9901–9925 of 15113 papers

TitleStatusHype
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
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs—0
Multi-agent Deep Covering Skill Discovery—0
Deep Multiagent Reinforcement Learning: Challenges and Directions—0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network—0
Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station—0
Multi-agent Deep Reinforcement Learning for Zero Energy Communities—0
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility—0
Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles—0
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings—0
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking—0
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Benchmark Results

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