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 12351–12400 of 15113 papers

TitleStatusHype
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
Convolutional Reservoir Computing for World ModelsCode0
Self-Attentional Credit Assignment for Transfer in Reinforcement LearningCode0
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery—0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Photonic architecture for reinforcement learning—0
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning—0
CADS: Core-Aware Dynamic Scheduler for Multicore Memory Controllers—0
An Inductive Synthesis Framework for Verifiable Reinforcement Learning—0
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
Ranking sentences from product description & bullets for better search—0
Mutual Reinforcement Learning—0
PPO Dash: Improving Generalization in Deep Reinforcement LearningCode0
Federated Reinforcement Distillation with Proxy Experience Memory—0
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning—0
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement LearningCode0
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative SamplingCode0
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
Imitation-Projected Programmatic Reinforcement Learning—0
DisCoRL: Continual Reinforcement Learning via Policy Distillation—0
Provably Efficient Reinforcement Learning with Linear Function ApproximationCode0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning FrameworkCode0
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances—0
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning—0
Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
Capturing Financial markets to apply Deep Reinforcement Learning—0
Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets—0
Better-than-Demonstrator Imitation Learning via Automatically-Ranked DemonstrationsCode0
Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping—0
On-Policy Robot Imitation Learning from a Converging Supervisor—0
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Variational Inference MPC for Bayesian Model-based Reinforcement Learning—0
Data Efficient Reinforcement Learning for Legged Robots—0
Deep Active Inference as Variational Policy GradientsCode0
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning—0
Intrinsic Motivation Driven Intuitive Physics Learning using Deep Reinforcement Learning with Intrinsic Reward Normalization—0
Playing Flappy Bird via Asynchronous Advantage Actor Critic Algorithm—0
On Inductive Biases in Deep Reinforcement Learning—0
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning—0
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes—0
Learning a Behavioral Repertoire from Demonstrations—0
Incrementally Learning Functions of the Return—0
Attentive Multi-Task Deep Reinforcement LearningCode0
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Benchmark Results

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