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 14651–14700 of 15113 papers

TitleStatusHype
Memory Lens: How Much Memory Does an Agent Use?—0
Options Discovery with Budgeted Reinforcement Learning—0
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games—0
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPUCode0
Learning to reinforcement learnCode0
Reinforcement Learning with Unsupervised Auxiliary TasksCode0
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based ModelsCode0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
Learning to Navigate in Complex EnvironmentsCode0
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods—0
RL^2: Fast Reinforcement Learning via Slow Reinforcement LearningCode0
Fairness in Reinforcement Learning—0
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control—0
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms—0
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning—0
Designing Neural Network Architectures using Reinforcement LearningCode0
Modular Multitask Reinforcement Learning with Policy SketchesCode0
Learning to Perform Physics Experiments via Deep Reinforcement Learning—0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Neural Architecture Search with Reinforcement LearningCode0
Multi-task learning with deep model based reinforcement learning—0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Quantile Reinforcement Learning—0
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?—0
Sample Efficient Actor-Critic with Experience ReplayCode1
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear—0
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Quantum-enhanced machine learning—0
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles—0
Utilization of Deep Reinforcement Learning for saccadic-based object visual search—0
A Reinforcement Learning Approach to the View Planning Problem—0
Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies—0
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
Reset-free Trial-and-Error Learning for Robot Damage RecoveryCode0
Sim-to-Real Robot Learning from Pixels with Progressive Nets—0
Introduction to the "Industrial Benchmark"—0
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation—0
Personalizing a Dialogue System with Transfer Reinforcement Learning—0
Multi-Objective Deep Reinforcement LearningCode0
Deep Reinforcement Learning From Raw Pixels in Doom—0
Active exploration in parameterized reinforcement learningCode0
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots—0
Connecting Generative Adversarial Networks and Actor-Critic Methods—0
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States—0
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search—0
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Benchmark Results

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