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 8776–8800 of 15113 papers

TitleStatusHype
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and SearchCode0
Predictive Control Using Learned State Space Models via Rolling Horizon Evolution—0
Reinforcement Learning for Mean Field Games, with Applications to Economics—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots—0
Density Constrained Reinforcement Learning—0
Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach—0
Bregman Gradient Policy OptimizationCode0
Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning—0
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving—0
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Reinforcement Learning for Physical Layer CommunicationsCode0
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation—0
Off-Policy Reinforcement Learning with Delayed Rewards—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations—0
Lifted Model Checking for Relational MDPs—0
Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations—0
Emphatic Algorithms for Deep Reinforcement Learning—0
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming—0
Analytically Tractable Bayesian Deep Q-Learning—0
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems—0
Policy Smoothing for Provably Robust Reinforcement Learning—0
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Benchmark Results

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