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 12701–12750 of 15113 papers

TitleStatusHype
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)—0
Knowledge Flow: Improve Upon Your Teachers—0
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey—0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman—0
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning—0
Teaching GANs to Sketch in Vector Format—0
Reinforced Imitation in Heterogeneous Action Space—0
Reinforcement Learning with Attention that Works: A Self-Supervised Approach—0
Multi-Preference Actor Critic—0
HOList: An Environment for Machine Learning of Higher-Order Theorem ProvingCode0
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks—0
PaintBot: A Reinforcement Learning Approach for Natural Media Painting—0
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement LearningCode0
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a SupercomputerCode0
Random Projection in Neural Episodic ControlCode0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents—0
Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization—0
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies—0
Guided Meta-Policy Search—0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
Dynamically Optimal Treatment AllocationCode0
Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading—0
Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning—0
Risk Averse Robust Adversarial Reinforcement Learning—0
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints—0
Autonomous Highway Driving using Deep Reinforcement Learning—0
Learning Good Representation via Continuous Attention—0
Improved Reinforcement Learning with Curriculum—0
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent—0
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach—0
Wasserstein Dependency Measure for Representation Learning—0
Regularizing Trajectory Optimization with Denoising Autoencoders—0
Meta-Learning surrogate models for sequential decision making—0
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?Code0
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning—0
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic RegressionCode0
Generalized Off-Policy Actor-Critic—0
Autoregressive Policies for Continuous Control Deep Reinforcement LearningCode0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
Energy Storage Management via Deep Q-Networks—0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus—0
Interactions between Representation Learning and Supervision—0
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies—0
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning—0
Sub-Task Discovery with Limited Supervision: A Constrained Clustering Approach—0
Show:102550
← PrevPage 255 of 303Next →

Benchmark Results

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