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

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Showing 13351–13400 of 15113 papers

TitleStatusHype
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning—0
Expressiveness in Deep Reinforcement Learning—0
Incremental Hierarchical Reinforcement Learning with Multitask LMDPs—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning—0
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Unsupervised Exploration with Deep Model-Based Reinforcement Learning—0
Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Solving Statistical Mechanics Using Variational Autoregressive NetworksCode1
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Controllable Neural Story Plot Generation via Reward ShapingCode0
Omega-Regular Objectives in Model-Free Reinforcement Learning—0
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Learning Navigation Behaviors End-to-End with AutoRL—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction—0
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting—0
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing—0
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation LearningCode0
Anderson Acceleration for Reinforcement Learning—0
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement LearningCode0
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection—0
Personalized Education at Scale—0
SDN Flow Entry Management Using Reinforcement Learning—0
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
On Reinforcement Learning for Full-length Game of StarCraft—0
A Learning Framework for High Precision Industrial Assembly—0
Geometric Multi-Model Fitting by Deep Reinforcement Learning—0
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration—0
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting—0
Constrained Exploration and Recovery from Experience ShapingCode0
Target Transfer Q-Learning and Its Convergence Analysis—0
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs—0
Dynamic Weights in Multi-Objective Deep Reinforcement LearningCode0
Benchmarking Reinforcement Learning Algorithms on Real-World RobotsCode0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes—0
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions—0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning—0
Automata Guided Reinforcement Learning With Demonstrations—0
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Benchmark Results

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