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 10826–10850 of 15113 papers

TitleStatusHype
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning—0
Reinforcement Learning as Iterative and Amortised Inference—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks—0
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning—0
Potential Field Guided Actor-Critic Reinforcement Learning—0
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed EnvironmentsCode0
Decorrelated Double Q-learning—0
Hierarchical reinforcement learning for efficent exploration and transfer—0
Exchangeable Models in Meta Reinforcement LearningCode0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning—0
Generalizing Curricula for Reinforcement Learning—0
Learning Intrinsically Motivated Options to Stimulate Policy Exploration—0
Deep Reinforcement Learning for Neural Control—0
A Brief Look at Generalization in Visual Meta-Reinforcement Learning—0
Logical Composition in Lifelong Reinforcement Learning—0
Bridging Worlds in Reinforcement Learning with Model-Advantage—0
Continuous Control for Searching and Planning with a Learned Model—0
Deep Reinforcement Learning for Electric Transmission Voltage Control—0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
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Benchmark Results

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