Aditya Mahajan

Doina Precup

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)

openreview.net

Model approximation in MDPs with unbounded per-step cost

Berk Bozkurt

Ashutosh Nayyar

Yi Ouyang

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process …

2024-02-13

ArXiv (preprint)

Bridging State and History Representations: Understanding Self-Predictive RL

Tianwei Ni

Benjamin Eysenbach

Erfan SeyedSalehi

Michel Ma

Clement Gehring

Pierre-Luc Bacon

Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (see more)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

On learning Whittle index policy for restless bandits with scalable regret

Nima Akbarzadeh

Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (see more)el is unknown. However, the cumulative regret of most RL algorithms scales as ˜ O(S

2024-01-01

IEEE Transactions on Control of Network Systems (published)

Strong Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Markov Jump Linear Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state obser… (see more)vations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-independent rate of convergence shows that, almost surely, the system identification error is

2024-01-01

IEEE Transactions on Automatic Control (published)

Two Families of Indexable Partially Observable Restless Bandits and Whittle Index Computation

Nima Akbarzadeh

2024-01-01

Perform. Evaluation (published)

Asymmetric Actor-Critic with Approximate Information State

Amit Sinha

Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to b… (see more)e made based on the entire history of observations and actions. However, in several scenarios, state information is available during the training phase. We are interested in exploiting the availability of this state information during the training phase to efficiently learn a history-based policy using RL. Specifically, we consider actor-critic algorithms, where the actor uses only the history information but the critic uses both history and state. Such algorithms are called asymmetric actor-critic, to highlight the fact that the actor and critic have asymmetric information. Motivated by the recent success of using representation losses in RL for POMDPs [1], we derive similar theoretical results for the asymmetric actor-critic case and evaluate the effectiveness of adding such auxiliary losses in experiments. In particular, we learn a history representation-called an approximate information state (AIS)-and bound the performance loss when acting using AIS.

2023-12-13

IEEE Conference on Decision and Control (published)

Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems

Borna Sayedana

Mohammad Afshari

Peter E. Caines

In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (see more)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of

2023-12-13

2023 62nd IEEE Conference on Decision and Control (CDC) (published)

Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost

Berk Bozkurt

Ashutosh Nayyar

Yi Ouyang

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …

2023-12-13

IEEE Conference on Decision and Control (published)

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amin Amini

Janarthanan Rajendran

Sarath Chandar Anbil Parthipan

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

Mean-field games among teams

Jayakumar Subramanian

Akshat Kumar

2023-10-18

ArXiv (preprint)

Decentralized Linear Quadratic Systems With Major and Minor Agents and Non-Gaussian Noise

Mohammad Afshari

A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor… (see more) agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise process is not assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that the major agent's optimal control action is a linear function of the major agent's minimum mean-squared error (MMSE) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a “correction term” that depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is non-Gaussian, the minor agent's MMSE estimate is a nonlinear function of its observation. It is shown that replacing the minor agent's MMSE estimate with its linear least mean square estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, common-information-based splitting of state and control actions, and simplifying the per-step cost based on conditional independence, orthogonality principle, and completion of squares.

2023-08-01

IEEE Transactions on Automatic Control (published)