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 11326–11350 of 15113 papers

TitleStatusHype
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Value-driven Hindsight Modelling—0
Optimistic Policy Optimization with Bandit Feedback—0
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning—0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge—0
Empirical Policy Evaluation with Supergraphs—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Multi-Issue Bargaining With Deep Reinforcement Learning—0
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding—0
Reinforcement learning for the privacy preservation and manipulation of eye tracking data—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Langevin DQNCode0
Control Frequency Adaptation via Action Persistence in Batch Reinforcement LearningCode0
Adaptive Experience Selection for Policy Gradient—0
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning—0
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling—0
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI—0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning—0
Robust Reinforcement Learning via Adversarial training with Langevin DynamicsCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks—0
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic—0
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Benchmark Results

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