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 13051–13100 of 15113 papers

TitleStatusHype
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Residual Policy LearningCode0
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous VehiclesCode2
Scaling shared model governance via model splitting—0
Dopamine: A Research Framework for Deep Reinforcement LearningCode3
Guaranteed satisficing and finite regret: Analysis of a cognitive satisficing value function—0
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems—0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Exploration Conscious Reinforcement Learning RevisitedCode0
Soft Actor-Critic Algorithms and ApplicationsCode1
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning—0
Efficient Model-Free Reinforcement Learning Using Gaussian Process—0
Dialogue Generation: From Imitation Learning to Inverse Reinforcement LearningCode0
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint—0
Learning Montezuma's Revenge from a Single Demonstration—0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning—0
Residual Reinforcement Learning for Robot Control—0
Off-Policy Deep Reinforcement Learning without ExplorationCode1
Measuring and Characterizing Generalization in Deep Reinforcement Learning—0
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCode0
Quantifying Generalization in Reinforcement LearningCode1
ToyBox: Better Atari Environments for Testing Reinforcement Learning AgentsCode0
Deep Reinforcement Learning and the Deadly Triad—0
Active Deep Q-learning with Demonstration—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Adapting Auxiliary Losses Using Gradient Similarity—0
Composing Entropic Policies using Divergence Correction—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
Relative Entropy Regularized Policy IterationCode0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
Exploration versus exploitation in reinforcement learning: a stochastic control approach—0
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning—0
Learning Vine Copula Models For Synthetic Data Generation—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model—0
Generative Adversarial Self-Imitation Learning—0
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach—0
Towards Solving Text-based Games by Producing Adaptive Action SpacesCode0
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic ControlCode0
Resource Constrained Deep Reinforcement Learning—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Mitigating Planner Overfitting in Model-Based Reinforcement Learning—0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Macro action selection with deep reinforcement learning in StarCraftCode0
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach—0
Simple random search of static linear policies is competitive for reinforcement learningCode0
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis—0
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning—0
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Benchmark Results

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