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 8376–8400 of 15113 papers

TitleStatusHype
Policy Optimization with Stochastic Mirror Descent—0
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space—0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios—0
Policy Search by Target Distribution Learning for Continuous Control—0
Policy Search for Motor Primitives in Robotics—0
Policy Search in Continuous Action Domains: an Overview—0
Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RL—0
Policy Shaping: Integrating Human Feedback with Reinforcement Learning—0
Policy Smoothing for Provably Robust Reinforcement Learning—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks—0
Policy Tree Network—0
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning—0
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Polyphonic Music Composition with LSTM Neural Networks and Reinforcement Learning—0
POMDP-lite for Robust Robot Planning under Uncertainty—0
POMRL: No-Regret Learning-to-Plan with Increasing Horizons—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning—0
Portfolio Management with Reinforcement Learning—0
Portfolio Optimization with 2D Relative-Attentional Gated Transformer—0
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning—0
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Benchmark Results

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