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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3426–3450 of 15113 papers

TitleStatusHype
Countering Language Drift via Grounding—0
A Study of Continual Learning Methods for Q-Learning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
A Study of AI Population Dynamics with Million-agent Reinforcement Learning—0
Counterfactual Regularization for Model-Based Reinforcement Learning—0
Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment—0
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
Deep learning for molecular design - a review of the state of the art—0
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Deep Learning Interference Cancellation in Wireless Networks—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment—0
Deep Learning of Koopman Representation for Control—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
DeepMDP: Learning Continuous Latent Space Models for Representation Learning—0
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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