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 12851–12900 of 15113 papers

TitleStatusHype
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
Random Projection in Neural Episodic ControlCode0
PaintBot: A Reinforcement Learning Approach for Natural Media Painting—0
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a SupercomputerCode0
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement LearningCode0
Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization—0
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents—0
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage DatasetCode0
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
Dynamically Optimal Treatment AllocationCode0
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach—0
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
Learning Good Representation via Continuous Attention—0
Autonomous Highway Driving using Deep Reinforcement Learning—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
Regularizing Trajectory Optimization with Denoising Autoencoders—0
Meta-Learning surrogate models for sequential decision making—0
Wasserstein Dependency Measure for Representation Learning—0
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?Code0
Autoregressive Policies for Continuous Control Deep Reinforcement LearningCode0
Generalized Off-Policy Actor-Critic—0
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic RegressionCode0
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning—0
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus—0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Energy Storage Management via Deep Q-Networks—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
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions—0
Neural Program Planner for Structured Predictions—0
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
Symbolic Regression Methods for Reinforcement Learning—0
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention—0
Jet grooming through reinforcement learningCode0
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction—0
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study—0
DQN with model-based exploration: efficient learning on environments with sparse rewards—0
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus—0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
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Benchmark Results

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