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Reinforcement learning algorithm for non-stationary environments

Applied intelligence (Dordrecht, Netherlands), 2020-11, Vol.50 (11), p.3590-3606

2020

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Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space

INFORMS journal on computing, 2018-01, Vol.30 (1), p.154-167

2018

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Novel First Order Bayesian Optimization with an Application to Reinforcement Learning

Applied intelligence (Dordrecht, Netherlands), 2021-03, Vol.51 (3), p.1565-1579

2021

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Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

IEEE transactions on intelligent transportation systems, 2021-01, Vol.22 (1), p.107-118

2021

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Stochastic Approximation With Iterate-Dependent Markov Noise Under Verifiable Conditions in Compact State Space With the Stability of Iterates Not Ensured

IEEE transactions on automatic control, 2021-12, Vol.66 (12), p.5941-5954

2021

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Two Time-Scale Stochastic Approximation with Controlled Markov Noise and Off-Policy Temporal-Difference Learning

Mathematics of operations research, 2018-02, Vol.43 (1), p.130-151

2018

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Stability of Stochastic Approximations With "Controlled Markov" Noise and Temporal Difference Learning

IEEE transactions on automatic control, 2019-06, Vol.64 (6), p.2614-2620

2019

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Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2019, p.1-6

2019

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On tight bounds for function approximation error in risk-sensitive reinforcement learning

Systems & control letters, 2021-04, Vol.150, p.104899, Article 104899

2021

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Analysis of Gradient Descent Methods With Nondiminishing Bounded Errors

IEEE transactions on automatic control, 2018-05, Vol.63 (5), p.1465-1471

2018

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Analyzing Approximate Value Iteration Algorithms

Mathematics of operations research, 2022-08, Vol.47 (3), p.2138-2159

2022

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A stability criterion for two timescale stochastic approximation schemes

Automatica (Oxford), 2017-05, Vol.79, p.108-114

2017

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An actor–critic algorithm with function approximation for discounted cost constrained Markov decision processes

Systems & control letters, 2010-12, Vol.59 (12), p.760-766

Bhatnagar, Shalabh

2010

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A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions

Mathematics of operations research, 2017-08, Vol.42 (3), p.648-661

2017

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A Linearly Relaxed Approximate Linear Program for Markov Decision Processes

IEEE transactions on automatic control, 2018-04, Vol.63 (4), p.1185-1191

2018

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Truncated Cauchy random perturbations for smoothed functional-based stochastic optimization

Automatica (Oxford), 2024-04, Vol.162, p.111528, Article 111528

2024

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Natural actor–critic algorithms

Automatica (Oxford), 2009-11, Vol.45 (11), p.2471-2482

2009

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A Generalized Minimax Q-Learning Algorithm for Two-Player Zero-Sum Stochastic Games

IEEE transactions on automatic control, 2022-09, Vol.67 (9), p.4816-4823

2022

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Stochastic Recursive Inclusions in Two Timescales with Nonadditive Iterate-Dependent Markov Noise

Mathematics of operations research, 2020-11, Vol.45 (4), p.1393

2020

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Generalized Second-Order Value Iteration in Markov Decision Processes

IEEE transactions on automatic control, 2022-08, Vol.67 (8), p.4241-4247

2022

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Adaptive mean queue size and its rate of change: queue management with random dropping

Telecommunication systems, 2017-06, Vol.65 (2), p.281-295

2017

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The Borkar–Meyn theorem for asynchronous stochastic approximations

Systems & control letters, 2011-07, Vol.60 (7), p.472-478

Bhatnagar, Shalabh

2011

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Efficient energy management in smart grids with finite horizon Q-learning

Sustainable Energy, Grids and Networks, 2024-06, Vol.38, p.101277, Article 101277

2024

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A stochastic approximation approach to active queue management

Telecommunication systems, 2018-05, Vol.68 (1), p.89-104

2018

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Asynchronous Stochastic Approximations With Asymptotically Biased Errors and Deep Multiagent Learning

IEEE transactions on automatic control, 2021-09, Vol.66 (9), p.3969-3983

2021

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Reinforcement learning algorithm for non-stationary environments

Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space

Novel First Order Bayesian Optimization with an Application to Reinforcement Learning

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

Stochastic Approximation With Iterate-Dependent Markov Noise Under Verifiable Conditions in Compact State Space With the Stability of Iterates Not Ensured

Two Time-Scale Stochastic Approximation with Controlled Markov Noise and Off-Policy Temporal-Difference Learning

Stability of Stochastic Approximations With "Controlled Markov" Noise and Temporal Difference Learning

Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots

On tight bounds for function approximation error in risk-sensitive reinforcement learning

Analysis of Gradient Descent Methods With Nondiminishing Bounded Errors

Analyzing Approximate Value Iteration Algorithms

A stability criterion for two timescale stochastic approximation schemes

An actor–critic algorithm with function approximation for discounted cost constrained Markov decision processes

A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions

A Linearly Relaxed Approximate Linear Program for Markov Decision Processes

Truncated Cauchy random perturbations for smoothed functional-based stochastic optimization

Natural actor–critic algorithms

A Generalized Minimax Q-Learning Algorithm for Two-Player Zero-Sum Stochastic Games

Stochastic Recursive Inclusions in Two Timescales with Nonadditive Iterate-Dependent Markov Noise

Generalized Second-Order Value Iteration in Markov Decision Processes

Adaptive mean queue size and its rate of change: queue management with random dropping

The Borkar–Meyn theorem for asynchronous stochastic approximations

Efficient energy management in smart grids with finite horizon Q-learning

A stochastic approximation approach to active queue management

Asynchronous Stochastic Approximations With Asymptotically Biased Errors and Deep Multiagent Learning

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