Portrait de Arushi Jain

Arushi Jain

Collaborateur·rice alumni - McGill
Superviseur⋅e principal⋅e
Doina Precup
Sujets de recherche
Apprentissage par renforcement
Site web
Google Scholar
GitHub

Publications

Adaptive Exploration for Data-Efficient General Value Function Evaluations
Arushi Jain
Josiah P. Hanna
Doina Precup
General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expecte… (voir plus)d return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning multiple GVFs in parallel using off-policy methods. To address this, we introduce GVFExplorer, which adaptively learns a single behavior policy that efficiently collects data for evaluating multiple GVFs in parallel. Our method optimizes the behavior policy by minimizing the total variance in return across GVFs, thereby reducing the required environmental interactions. We use an existing temporal-difference-style variance estimator to approximate the return variance. We prove that each behavior policy update decreases the overall mean squared error in GVF predictions. We empirically show our method's performance in tabular and nonlinear function approximation settings, including Mujoco environments, with stationary and non-stationary reward signals, optimizing data usage and reducing prediction errors across multiple GVFs.
2024-09-24
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain
Sharan Vaswani
Reza Babanezhad Harikandeh
Csaba Szepesvari
Doina Precup
We study policy optimization in an infinite horizon, …
2021-12-31
UAI (publié)
doi.org
proceedings.mlr.press
Variance Penalized On-Policy and Off-Policy Actor-Critic
Arushi Jain
Gandharv Patil
Ayush Jain
Khimya Khetarpal
Doina Precup
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applic… (voir plus)ations, low variance in the return is desired to ensure the reliability of an algorithm. In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a performance criterion involving both mean and variance in the return. Previous work uses the second moment of return to estimate the variance indirectly. Instead, we use a much simpler recently proposed direct variance estimator which updates the estimates incrementally using temporal difference methods. Using the variance-penalized criterion, we guarantee the convergence of our algorithm to locally optimal policies for finite state action Markov decision processes. We demonstrate the utility of our algorithm in tabular and continuous MuJoCo domains. Our approach not only performs on par with actor-critic and prior variance-penalization baselines in terms of expected return, but also generates trajectories which have lower variance in the return.
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain
Khimya Khetarpal
Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, … (voir plus)facilitates a better understanding of an agent's decisions. We tackle this problem in the options framework, a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state-space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviour of the proposed approach in a tabular grid-world, continuous-state puddle-world, and three games from the Arcade Learning Environment: Ms.Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favorably both with primitive actions as well as with risk-neutral options.
2021-04-06
The Knowledge Engineering Review (publié)
doi.org
arxiv.org