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05/20/2023
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
The average reward criterion is relatively less studied as most existing...
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05/20/2023
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
We present a novel algorithm for training deep neural networks in superv...
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04/21/2023
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
We consider the problem of control in the setting of reinforcement learn...
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03/13/2023
n-Step Temporal Difference Learning with Optimal n
We consider the problem of finding the optimal value of n in the n-step ...
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12/20/2022
Generalized Simultaneous Perturbation Stochastic Approximation with Reduced Estimator Bias
We present in this paper a family of generalized simultaneous perturbati...
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10/14/2022
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
During initial iterations of training in most Reinforcement Learning (RL...
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10/10/2022
A policy gradient approach for Finite Horizon Constrained Markov Decision Processes
The infinite horizon setting is widely adopted for problems of reinforce...
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10/10/2022
Actor-Critic or Critic-Actor? A Tale of Two Time Scales
We revisit the standard formulation of tabular actor-critic algorithm as...
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07/30/2022
A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization
In this paper, we present a stochastic gradient algorithm for minimizing...
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01/02/2022
Reinforcement Learning for Task Specifications with Action-Constraints
In this paper, we use concepts from supervisory control theory of discre...
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11/23/2021
Schedule Based Temporal Difference Algorithms
Learning the value function of a given policy from data samples is an im...
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11/22/2021
Gradient Temporal Difference with Momentum: Stability and Convergence
Gradient temporal difference (Gradient TD) algorithms are a popular clas...
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10/19/2021
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm
Learning optimal behavior from existing data is one of the most importan...
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01/07/2021
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning
In this work, we consider the problem of computing optimal actions for R...
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10/30/2020
Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach
In this paper, with a view toward fast deployment of locomotion gaits in...
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10/09/2020
Hindsight Experience Replay with Kronecker Product Approximate Curvature
Hindsight Experience Replay (HER) is one of the efficient algorithm to s...
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09/02/2020
A reinforcement learning approach to hybrid control design
In this paper we design hybrid control policies for hybrid systems whose...
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07/28/2020
Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations
With the research into development of quadruped robots picking up pace, ...
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02/06/2020
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks
We consider the problem of energy management in microgrid networks. A mi...
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12/30/2019
Gait Library Synthesis for Quadruped Robots via Augmented Random Search
In this paper, with a view toward fast deployment of learned locomotion ...
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11/20/2019
Hierarchical Average Reward Policy Gradient Algorithms
Option-critic learning is a general-purpose reinforcement learning (RL) ...
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11/13/2019
A Convergent Off-Policy Temporal Difference Algorithm
Learning the value function of a given policy (target policy) from the d...
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11/01/2019
Generalized Speedy Q-learning
In this paper, we derive a generalization of the Speedy Q-learning (SQL)...
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06/16/2019
Solution of Two-Player Zero-Sum Game by Successive Relaxation
We consider the problem of two-player zero-sum game. In this setting, th...
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05/15/2019
Learning Active Spine Behaviors for Dynamic and Efficient Locomotion in Quadruped Robots
In this work, we provide a simulation framework to perform systematic st...
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05/10/2019
Reinforcement Learning in Non-Stationary Environments
Reinforcement learning (RL) methods learn optimal decisions in the prese...
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05/10/2019
Second Order Value Iteration in Reinforcement Learning
Value iteration is a fixed point iteration technique utilized to obtain ...
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05/08/2019
Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning
In cooperative stochastic games multiple agents work towards learning jo...
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03/09/2019
Successive Over Relaxation Q-Learning
In a discounted reward Markov Decision Process (MDP) the objective is to...
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02/11/2019
An Online Sample Based Method for Mode Estimation using ODE Analysis of Stochastic Approximation Algorithms
One of the popular measures of central tendency that provides better rep...
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01/03/2019
Design, Development and Experimental Realization of a Quadrupedal Research Platform: Stoch
In this paper, we present a complete description of the hardware design ...
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11/08/2018
Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge
This paper presents our method for enabling a UAV quadrotor, equipped wi...
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10/09/2018
Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives
Humans and animals are believed to use a very minimal set of trajectorie...
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08/08/2018
Random directions stochastic approximation with deterministic perturbations
We introduce deterministic perturbation schemes for the recently propose...
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06/15/2018
An Online Prediction Algorithm for Reinforcement Learning with Linear Function Approximation using Cross Entropy Method
In this paper, we provide two new stable online algorithms for the probl...
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02/22/2018
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Asynchronous stochastic approximations are an important class of model-f...
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01/31/2018
A Cross Entropy based Optimization Algorithm with Global Convergence Guarantees
The cross entropy (CE) method is a model based search method to solve op...
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01/31/2018
An Incremental Off-policy Search in a Model-free Markov Decision Process Using a Single Sample Path
In this paper, we consider a modified version of the control problem in ...
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11/17/2017
RLWS: A Reinforcement Learning based GPU Warp Scheduler
The Streaming Multiprocessors (SMs) of a Graphics Processing Unit (GPU) ...
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11/14/2017
A unified decision making framework for supply and demand management in microgrid networks
This paper considers two important problems - on the supply-side and dem...
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09/14/2017
Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations
The main aim of this paper is the development of easily verifiable suffi...
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08/27/2017
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
We consider the problem of tracking an intruder using a network of wirel...
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08/25/2017
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
We consider the problem of minimizing the difference in the demand and t...
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04/01/2016
Analysis of gradient descent methods with non-diminishing, bounded errors
The main aim of this paper is to provide an analysis of gradient descent...
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11/27/2015
Shaping Proto-Value Functions via Rewards
In this paper, we combine task-dependent reward shaping and task-indepen...
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04/23/2015
Stability of Stochastic Approximations with `Controlled Markov' Noise and Temporal Difference Learning
In this paper we present a `stability theorem' for stochastic approximat...
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03/31/2015
Two Timescale Stochastic Approximation with Controlled Markov noise and Off-policy temporal difference learning
We present for the first time an asymptotic convergence analysis of two ...
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02/06/2015
Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem
In this paper we present a framework to analyze the asymptotic behavior ...
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02/06/2015
A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions
In this paper the stability theorem of Borkar and Meyn is extended to in...
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01/08/2014