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 9751–9800 of 15113 papers

TitleStatusHype
Model-based reinforcement learning for protein backbone design—0
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows—0
Model Based Reinforcement Learning for Atari—0
Model-Based Reinforcement Learning for Sepsis Treatment—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Whole-Chain Recommendations—0
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games—0
Model-Based Reinforcement Learning via Imagination with Derived Memory—0
Model-Based Reinforcement Learning via Meta-Policy Optimization—0
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration—0
Model-based Reinforcement Learning with Ensembled Model-value Expansion—0
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation—0
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network—0
Model-Based Reinforcement Learning with SINDy—0
Model-Based Reinforcement Learning with Value-Targeted Regression—0
Model Based Residual Policy Learning with Applications to Antenna Control—0
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds—0
Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning—0
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
Model Embedding Model-Based Reinforcement Learning—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning—0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning—0
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Model-Free Episodic Control with State Aggregation—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Model-Free Imitation Learning with Policy Optimization—0
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee—0
Model-Free Learning of Safe yet Effective Controllers—0
Model-Free Linear Quadratic Control via Reduction to Expert Prediction—0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Model-Free μ Synthesis via Adversarial Reinforcement Learning—0
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning—0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
Model-free optimal control of discrete-time systems with additive and multiplicative noises—0
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents—0
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey—0
Model-free Reinforcement Learning for Stochastic Stackelberg Security Games—0
Model-free Reinforcement Learning for Branching Markov Decision Processes—0
Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
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Benchmark Results

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