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

Showing 66516700 of 15113 papers

TitleStatusHype
Virtual Replay CacheCode0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning0
MDPFuzz: Testing Models Solving Markov Decision Processes0
Distilled Domain Randomization0
Deep differentiable reinforcement learning and optimal trading0
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Functional Regularization for Reinforcement Learning via Learned Fourier FeaturesCode1
Flexible Option LearningCode0
Lecture Notes on Partially Known MDPs0
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning0
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radarsCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Deep Policy Iteration with Integer Programming for Inventory Management0
Reinforcement learning for options on target volatility funds0
Reinforcement Learning-Based Automatic Berthing SystemCode1
An Analytical Update Rule for General Policy Optimization0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning0
Convergence Guarantees for Deep Epsilon Greedy Policy Learning0
Differentially Private Exploration in Reinforcement Learning with Linear Representation0
Towards Interactive Reinforcement Learning with Intrinsic Feedback0
A Generic Graph Sparsification Framework using Deep Reinforcement LearningCode0
Towards Personalization of User Preferences in Partially Observable Smart Home Environments0
Reward-Free Attacks in Multi-Agent Reinforcement Learning0
Maximum Entropy Model-based Reinforcement Learning0
Sample Complexity of Robust Reinforcement Learning with a Generative ModelCode0
Safe Reinforcement Learning for Grid Voltage Control0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems0
Architecting and Visualizing Deep Reinforcement Learning Models0
Design and Development of Spoken Dialogue System in Indic Languages0
NEORL: NeuroEvolution Optimization with Reinforcement LearningCode1
On the Practical Consistency of Meta-Reinforcement Learning Algorithms0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems0
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement LearningCode0
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation0
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat0
Improving gearshift controllers for electric vehicles with reinforcement learning0
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Fast Algorithms for L_-constrained S-rectangular Robust MDPs0
Structural Credit Assignment in Neural Networks using Reinforcement Learning0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning0
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization0
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
Model-Based Reinforcement Learning via Imagination with Derived Memory0
Reinforcement Learning in Newcomblike Environments0
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision ProcessesCode0
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Benchmark Results

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