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

Showing 47514800 of 15113 papers

TitleStatusHype
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming0
An Understanding of Learning from Demonstrations for Neural Text Generation0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework0
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology0
Anytime PSRO for Two-Player Zero-Sum Games0
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning0
AoI Minimization in Status Update Control with Energy Harvesting Sensors0
A PAC RL Algorithm for Episodic POMDPs0
A pedestrian path-planning model in accordance with obstacle's danger with reinforcement learning0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework0
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning0
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks0
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving0
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning0
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design0
Application of deep reinforcement learning for Indian stock trading automation0
Application of Deep Reinforcement Learning to Payment Fraud0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids0
Application of Reinforcement Learning for 5G Scheduling Parameter Optimization0
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control0
Applications of Deep Learning and Reinforcement Learning to Biological Data0
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey0
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review0
Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network0
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World0
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods0
Approximate discounting-free policy evaluation from transient and recurrent states0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning0
Approximate Dynamic Programming For Linear Systems with State and Input Constraints0
Approximate Equivariance in Reinforcement Learning0
A policy gradient approach for optimization of smooth risk measures0
Approximate information state based convergence analysis of recurrent Q-learning0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning0
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning0
Approximated Multi-Agent Fitted Q Iteration0
Does DQN Learn?0
Approximate Robust NMPC using Reinforcement Learning0
Approximating a deep reinforcement learning docking agent using linear model trees0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning0
Approximating Euclidean by Imprecise Markov Decision Processes0
Approximating Martingale Process for Variance Reduction in Deep Reinforcement Learning with Large State Space0
Approximating meta-heuristics with homotopic recurrent neural networks0
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems0
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning0
APRIL: Active Preference-learning based Reinforcement Learning0
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee0
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Benchmark Results

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