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06/13/2022
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward
The remarkable success of reinforcement learning (RL) heavily relies on ...
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02/07/2022
Model-Based Offline Meta-Reinforcement Learning with Regularization
Existing offline reinforcement learning (RL) methods face a few major ch...
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10/20/2021
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
The problem of constrained Markov decision process (CMDP) is investigate...
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10/13/2021
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method
Emphatic temporal difference (ETD) learning (Sutton et al., 2016) is a s...
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07/06/2021
A Unified Off-Policy Evaluation Approach for General Value Function
General Value Function (GVF) is a powerful tool to represent both the pr...
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02/23/2021
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
Designing off-policy reinforcement learning algorithms is typically a ve...
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02/09/2021
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
The gradient descent-ascent (GDA) algorithm has been widely applied to s...
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11/11/2020
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis
Safe reinforcement learning (SRL) problems are typically modeled as cons...
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11/10/2020
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms
Two timescale stochastic approximation (SA) has been widely used in valu...
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06/24/2020
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
Generative adversarial imitation learning (GAIL) is a popular inverse re...
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06/16/2020
Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization
Min-max optimization captures many important machine learning problems s...
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05/07/2020
Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms
As an important type of reinforcement learning algorithms, actor-critic ...
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04/27/2020
Improving Sample Complexity Bounds for Actor-Critic Algorithms
The actor-critic (AC) algorithm is a popular method to find an optimal p...
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02/15/2020
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling
Despite the wide applications of Adam in reinforcement learning (RL), th...
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01/07/2020
Reanalysis of Variance Reduced Temporal Difference Learning
Temporal difference (TD) learning is a popular algorithm for policy eval...
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09/26/2019
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
Gradient-based temporal difference (GTD) algorithms are widely used in o...
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02/06/2019
Finite-Sample Analysis for SARSA and Q-Learning with Linear Function Approximation
Though the convergence of major reinforcement learning algorithms has be...
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06/12/2018
