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 11601–11650 of 15113 papers

TitleStatusHype
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement LearningCode0
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning—0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Quinoa: a Q-function You Infer Normalized Over Actions—0
Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices—0
Robotic Tracking Control with Kernel Trick-based Reinforcement Learning—0
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms—0
Learning from Trajectories via Subgoal DiscoveryCode0
Non-Cooperative Inverse Reinforcement Learning—0
Online Robustness Training for Deep Reinforcement Learning—0
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs—0
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning—0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Explicit Explore-Exploit Algorithms in Continuous State SpacesCode0
Frequentist Regret Bounds for Randomized Least-Squares Value IterationCode0
Generating Formality-Tuned Summaries Using Input-Dependent Rewards—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference—0
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning—0
Generalized Speedy Q-learningCode0
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning—0
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances—0
Exploring Diverse Expressions for Paraphrase Generation—0
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning—0
Positive-Unlabeled Reward Learning—0
Neural Topic Model with Reinforcement Learning—0
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Hierarchical Expert Networks for Meta-Learning—0
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
RLINK: Deep Reinforcement Learning for User Identity Linkage—0
Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven DialogueCode0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning—0
RBED: Reward Based Epsilon Decay—0
Policy Continuation with Hindsight Inverse DynamicsCode0
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
Feedback Linearization for Unknown Systems via Reinforcement Learning—0
Learning to Manipulate Deformable Objects without DemonstrationsCode1
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and ExperimentsCode0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps—0
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization—0
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Benchmark Results

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