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

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Showing 9501–9550 of 15113 papers

TitleStatusHype
MAP Inference for Bayesian Inverse Reinforcement Learning—0
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments—0
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System—0
Marginalized Importance Sampling for Off-Environment Policy Evaluation—0
Marginalized Operators for Off-policy Reinforcement Learning—0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Market Making via Reinforcement Learning in China Commodity Market—0
Markov Chain Concentration with an Application in Reinforcement Learning—0
Markov Chain Monte Carlo Policy Optimization—0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket—0
Markov Decision Processes with Continuous Side Information—0
Markovian Interference in Experiments—0
MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
MARTI-4: new model of human brain, considering neocortex and basal ganglia -- learns to play Atari game by reinforcement learning on a single CPU—0
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks—0
Masked Deep Q-Recommender for Effective Question Scheduling—0
Masked Generative Priors Improve World Models Sequence Modelling Capabilities—0
Masked World Models for Visual Control—0
MASP: Scalable GNN-based Planning for Multi-Agent Navigation—0
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning—0
Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning—0
Mastering Spatial Graph Prediction of Road Networks—0
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning—0
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning—0
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning—0
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI—0
Deep Policy Iteration with Integer Programming for Inventory Management—0
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning—0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Maximizing Confidence Alone Improves Reasoning—0
Maximizing Ensemble Diversity in Deep Reinforcement Learning—0
Maximizing Information Gain in Partially Observable Environments via Prediction Reward—0
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons—0
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning—0
Maximum Entropy Dueling Network Architecture in Atari Domain—0
Maximum entropy GFlowNets with soft Q-learning—0
Maximum Entropy Hindsight Experience Replay—0
Adversarial Inverse Reinforcement Learning for Mean Field Games—0
Maximum Entropy Model-based Reinforcement Learning—0
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors—0
Maximum Entropy Reinforcement Learning with Mixture Policies—0
Maximum Entropy RL (Provably) Solves Some Robust RL Problems—0
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning—0
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees—0
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Benchmark Results

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