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

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Showing 15051–15100 of 15113 papers

TitleStatusHype
APRIL: Active Preference-learning based Reinforcement Learning—0
The Arcade Learning Environment: An Evaluation Platform for General AgentsCode0
Reinforcement Learning of Question-Answering Dialogue Policies for Virtual Museum Guides—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Monte Carlo Bayesian Reinforcement Learning—0
Policy Gradients with Variance Related Risk Criteria—0
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods—0
Off-Policy Actor-CriticCode0
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search—0
Evaluation of Online Dialogue Policy Learning Techniques—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Multi-timescale Nexting in a Reinforcement Learning Robot—0
Reinforcement Learning using Kernel-Based Stochastic Factorization—0
Transfer from Multiple MDPs—0
MAP Inference for Bayesian Inverse Reinforcement Learning—0
Selecting the State-Representation in Reinforcement Learning—0
Optimal Reinforcement Learning for Gaussian Systems—0
Policy Gradient Coagent Networks—0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Action-Gap Phenomenon in Reinforcement Learning—0
Blending Autonomous Exploration and Apprenticeship Learning—0
A Reinforcement Learning Theory for Homeostatic Regulation—0
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery—0
Analysis and Improvement of Policy Gradient Estimation—0
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
Predictive State Temporal Difference Learning—0
LSTD with Random Projections—0
Nonparametric Bayesian Policy Priors for Reinforcement Learning—0
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories—0
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains—0
Feature Construction for Inverse Reinforcement Learning—0
Double Q-learning—0
Linear Complementarity for Regularized Policy Evaluation and Improvement—0
Fast Reinforcement Learning for Energy-Efficient Wireless Communications—0
Reinforcement Learning via AIXI Approximation—0
Computational Model of Music Sight Reading: A Reinforcement Learning Approach—0
Feature Selection as a One-Player Game—0
A Generalized Natural Actor-Critic Algorithm—0
Discrete MDL Predicts in Total Variation—0
Solving Stochastic Games—0
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability—0
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference—0
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining—0
A Monte Carlo AIXI ApproximationCode0
Hebbian Learning of Bayes Optimal Decisions—0
Near-optimal Regret Bounds for Reinforcement Learning—0
Optimization on a Budget: A Reinforcement Learning Approach—0
Policy Search for Motor Primitives in Robotics—0
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Benchmark Results

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