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 12201–12250 of 15113 papers

TitleStatusHype
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
Mutual Reinforcement Learning—0
PPO Dash: Improving Generalization in Deep Reinforcement LearningCode0
Ranking sentences from product description & bullets for better search—0
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
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Imitation-Projected Programmatic Reinforcement Learning—0
A Model-based Approach for Sample-efficient Multi-task 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
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning—0
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances—0
Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles—0
Assessing Transferability from Simulation to Reality for Reinforcement Learning—0
Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning FrameworkCode0
An Optimistic Perspective on Offline Reinforcement LearningCode1
Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping—0
Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets—0
Capturing Financial markets to apply Deep Reinforcement Learning—0
Better-than-Demonstrator Imitation Learning via Automatically-Ranked DemonstrationsCode0
Variational Inference MPC for Bayesian Model-based Reinforcement Learning—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
Deep Active Inference as Variational Policy GradientsCode0
Data Efficient Reinforcement Learning for Legged Robots—0
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
Incrementally Learning Functions of the Return—0
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes—0
Attentive Multi-Task Deep Reinforcement LearningCode0
Learning a Behavioral Repertoire from Demonstrations—0
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning—0
On Inductive Biases in Deep Reinforcement Learning—0
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation—0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
Benchmarking Model-Based Reinforcement LearningCode0
Reasoning and Generalization in RL: A Tool Use PerspectiveCode0
Perspective Taking in Deep Reinforcement Learning Agents—0
On the Weaknesses of Reinforcement Learning for Neural Machine Translation—0
Playing Go without Game Tree Search Using Convolutional Neural Networks—0
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Benchmark Results

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