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 9001–9025 of 15113 papers

TitleStatusHype
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
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Positive-Unlabeled Reward Learning—0
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning—0
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
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Benchmark Results

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