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 9526–9550 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 382 of 605Next →

Benchmark Results

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