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 10576–10600 of 15113 papers

TitleStatusHype
Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games—0
A reinforcement learning approach to rare trajectory samplingCode0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model—0
Deep Reinforcement Learning Based Power Allocation for D2D Network—0
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOCode1
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks—0
Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications—0
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic—0
Policy Entropy for Out-of-Distribution Classification—0
Gradient Monitored Reinforcement Learning—0
Deep Learning Models for Automatic Summarization—0
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce—0
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning—0
Automatic Discovery of Interpretable Planning StrategiesCode0
Model-free Reinforcement Learning for Stochastic Stackelberg Security Games—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning—0
Reinforcement learning with human advice: a survey—0
Evaluating Generalisation in General Video Game Playing—0
Q-NAV: NAV Setting Method based on Reinforcement Learning in Underwater Wireless Networks—0
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension—0
Novel Policy Seeking with Constrained OptimizationCode0
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Benchmark Results

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