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 10276–10300 of 15113 papers

TitleStatusHype
Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion—0
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search—0
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation—0
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning—0
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning—0
Efficient collective swimming by harnessing vortices through deep reinforcement learning—0
Efficient Competitive Self-Play Policy Optimization—0
Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient decorrelation of features using Gramian in Reinforcement Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Efficient Duple Perturbation Robustness in Low-rank MDPs—0
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Efficient entity-based reinforcement learning—0
Efficient Entropy for Policy Gradient with Multidimensional Action Space—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Efficient Exploration and Value Function Generalization in Deterministic Systems—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
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Benchmark Results

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