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 13201–13250 of 15113 papers

TitleStatusHype
Composing Entropic Policies using Divergence Correction—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
Relative Entropy Regularized Policy IterationCode0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning—0
Learning Vine Copula Models For Synthetic Data Generation—0
Exploration versus exploitation in reinforcement learning: a stochastic control approach—0
Generative Adversarial Self-Imitation Learning—0
FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model—0
Deep Reinforcement Learning for Intelligent Transportation Systems—0
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach—0
Resource Constrained Deep Reinforcement Learning—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Towards Solving Text-based Games by Producing Adaptive Action SpacesCode0
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic ControlCode0
Mitigating Planner Overfitting in Model-Based Reinforcement Learning—0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Macro action selection with deep reinforcement learning in StarCraftCode0
Simple random search of static linear policies is competitive for reinforcement learningCode0
Transfer of Value Functions via Variational Methods—0
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach—0
The Importance of Sampling inMeta-Reinforcement Learning—0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Temporal Regularization for Markov Decision ProcessCode0
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making—0
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis—0
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning—0
Learning Curriculum Policies for Reinforcement LearningCode0
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning—0
Data center cooling using model-predictive controlCode0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL—0
Genetic-Gated Networks for Deep Reinforcement Learning—0
Constrained Cross-Entropy Method for Safe Reinforcement Learning—0
Distributed Multitask Reinforcement Learning with Quadratic Convergence—0
Geometrically Coupled Monte Carlo Sampling—0
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning—0
Deep Multi-Agent Reinforcement Learning with Relevance GraphsCode0
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology—0
Modeling natural language emergence with integral transform theory and reinforcement learningCode0
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL—0
Modulated Policy Hierarchies—0
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning—0
A Structure-aware Online Learning Algorithm for Markov Decision Processes—0
Deep Reinforcement Learning for Autonomous DrivingCode0
Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge—0
Unsupervised Control Through Non-Parametric Discriminative Rewards—0
Trajectory-based Learning for Ball-in-Maze Games—0
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems—0
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
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Benchmark Results

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