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 1851–1875 of 15113 papers

TitleStatusHype
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Long-Term Planning with Deep Reinforcement Learning on Autonomous DronesCode1
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter OptimizationCode1
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic ControlCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement LearningCode1
Enhancing SAT solvers with glue variable predictionsCode1
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningCode1
LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement LearningCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Meta-Learning through Hebbian Plasticity in Random NetworksCode1
Bidirectional Model-based Policy OptimizationCode1
Reward Machines for Cooperative Multi-Agent Reinforcement LearningCode1
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning ApproachCode1
Reinforcement Learning based Control of Imitative Policies for Near-Accident DrivingCode1
Debiased Contrastive LearningCode1
Evaluating the Performance of Reinforcement Learning AlgorithmsCode1
MDP Homomorphic Networks: Group Symmetries in Reinforcement LearningCode1
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEsCode1
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Intrinsic Reward Driven Imitation Learning via Generative ModelCode1
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Benchmark Results

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