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 9201–9250 of 15113 papers

TitleStatusHype
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Smoothing Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios—0
Train a snake with reinforcement learning algorithms—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement LearningCode0
Optimal Portfolio Liquidation—0
Virtual Autonomous Driving with Reinforcement Learning—0
Reinforcement Learning in 20Q Game with Generic Knowledge Bases—0
Mobile Robots Autonomous Exploration with Reinforcement Learning—0
Towards Understanding Deep Policy Gradients: A Case Study on PPO—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Optimization of Multi-Factor Model in Quantitative Trading Based On Reinforcement Learning—0
Reinforcement Learning for Contact-Rich Tasks: Robotic Peg Insertion StrategiesCode1
Ranking Items in Large-Scale Item Search Engines with Reinforcement Learning—0
Mobile Robots Exploration via Deep Reinforcement Learning—0
Reinforcement Learning Based Adaptive WalkingAssistance Control of a Lower Limb Exoskeleton—0
Reinforcement Learning for Predict+Optimize—0
Portfolio Management with Reinforcement Learning—0
Reinforcement Learning Based Character Controlling—0
Increasing Data Efficiency of Driving Agent By World ModelCode0
Cloud Database Tuning with Reinforcement LearningCode0
Learn to Play Tetris with Deep Reinforcement Learning—0
Evading Web Application Firewalls with Reinforcement Learning—0
IPM Move Planner: AN EFFICIENT EXPLOITING DEEP REINFORCEMENT LEARNING WITH MONTE CARLO TREE SEARCH—0
Learn To Manage Portfolio With Reinforcement Learning—0
Demystify Painting with RL—0
Deploying Reinforcement Learning in Water Transport—0
Automatic Source Code Summarization via Reinforcement Learning—0
Learning Mobile Robot Navigation in the Dense Crowd with Deep Reinforcement Learning—0
A case for new neural network smoothness constraints—0
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Policy Gradient RL Algorithms as Directed Acyclic GraphsCode1
Sim-to-real reinforcement learning applied to end-to-end vehicle controlCode1
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
Learning for MPC with Stability & Safety Guarantees—0
Active Hierarchical Imitation and Reinforcement Learning—0
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability—0
Evolutionary learning of interpretable decision treesCode0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Tutoring Reinforcement Learning via Feedback Control—0
Semi-supervised reward learning for offline reinforcement learning—0
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks—0
Regularizing Action Policies for Smooth Control with Reinforcement Learning—0
OPAC: Opportunistic Actor-Critic—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
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Benchmark Results

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