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 7976–8000 of 15113 papers

TitleStatusHype
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images—0
Multiple-objective Reinforcement Learning for Inverse Design and Identification—0
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning—0
Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning—0
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task—0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
Multi-Preference Actor Critic—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
Multiqubit and multilevel quantum reinforcement learning with quantum technologies—0
Multi-Radar Tracking Optimization for Collaborative Combat—0
Multi-resolution Exploration in Continuous Spaces—0
Multi-Reward based Reinforcement Learning for Neural Machine Translation—0
Multi-Robot Active Mapping via Neural Bipartite Graph Matching—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning—0
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots—0
Multi-Robot Formation Control Using Reinforcement Learning—0
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning—0
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning—0
Multi-step Greedy Reinforcement Learning Algorithms—0
Multi-step Reinforcement Learning: A Unifying Algorithm—0
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Benchmark Results

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