Riashat Islam

Siddhartha Sen

John Langford

Katja Hofmann

Sergio Valcarcel Macua

Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent wo… (voir plus)rks have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics model (IDM). While PIDM often outperforms BC, the reasons behind its benefits remain unclear. In this paper, we provide a theoretical explanation: PIDM introduces a bias-variance tradeoff. While predicting the future state introduces bias, conditioning the IDM on the prediction can significantly reduce variance. We establish conditions on the state predictor bias for PIDM to achieve lower prediction error and higher sample efficiency than BC, with the gap widening when additional data sources are available. We validate the theoretical insights empirically in 2D navigation tasks, where BC requires up to five times (three times on average) more demonstrations than PIDM to reach comparable performance; and in a complex 3D environment in a modern video game with high-dimensional visual inputs and stochastic transitions, where BC requires over 66\% more samples than PIDM.

2025-12-31

International Conference on Machine Learning (Accept (regular))

openreview.net

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

Guillaume Huguet

James Vuckovic

Kilian Fatras

Éric Thibodeau-Laufer

Cheng-Hao Liu

Michael Bronstein

Avishek Joey Bose

2024-12-10

Neural Information Processing Systems (Accept (poster))

openreview.net

Reinforcement Learning for Sequence Design Leveraging Protein Language Models

Jithendaraa Subramanian

Shiva Kanth Sujit

Niloy Irtisam

Umong Sain

D. Nowrouzezahrai

S Ebrahimi Kahou

2024-07-02

ArXiv (prépublication)

Offline Policy Optimization in RL with Variance Regularizaton

Samarth Sinha

Homanga Bharadhwaj

Samin Yeasar Arnob

Zhuoran Yang

Animesh Garg

Zhaoran Wang

Lihong Li

2022-12-28

arXiv (prépublication)

Discrete Factorial Representations as an Abstraction for Goal Conditioned RL

Hongyu Zang

Xin Li

Romain Laroche

Yoshua Bengio

Remi Tachet des Combes

2022-11-28

Conference on Neural Information Processing Systems (Accept)

openreview.net

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Hongyu Zang

Xin Li

Romain Laroche

Yoshua Bengio

Remi Tachet des Combes

2022-10-31

arXiv (prépublication)

Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning

Samin Yeasar Arnob

2021-12-30

ArXiv (prépublication)

Randomized Least Squares Policy Optimization

Haque Ishfaq

Zhuoran Yang

Andrei Lupu

Viet Bang Nguyen

Lewis Liu

Zhaoran Wang

Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. … (voir plus)However, designing provably efﬁcient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper conﬁdence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efﬁcient PO algorithms.

2020-12-31

(publié)

www.semanticscholar.org

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods

The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, t… (voir plus)his results in policies that are optimal for one distribution over initial states, but may not be uniformly optimal for others, no matter where the agent starts from. Furthermore, to obtain unbiased gradient estimates, the starting point of the policy gradient estimator requires sampling states from a normalized discounted weighting of states. However, the difficulty of estimating the normalized discounted weighting of states, or the stationary state distribution, is quite well-known. Additionally, the large sample complexity of policy gradient methods is often attributed to insufficient exploration, and to remedy this, it is often assumed that the restart distribution provides sufficient exploration in these algorithms. In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution. The key contribution of our work includes providing a practically feasible algorithm to estimate the normalized discounted weighting of states, i.e, the \textit{discounted future state distribution}. We propose that exploration can be achieved by entropy regularization with the discounted state distribution in policy gradients, where a metric for maximal coverage of the state space can be based on the entropy of the induced state distribution. The proposed approach can be considered as a three time-scale algorithm and under some mild technical conditions, we prove its convergence to a locally optimal policy. Experimentally, we demonstrate usefulness of regularization with the discounted future state distribution in terms of increased state space coverage and faster learning on a range of complex tasks.

2019-12-10

ArXiv (prépublication)

Marginalized State Distribution Entropy Regularization in Policy Optimization

Zafarali Ahmed

2019-12-10

ArXiv (prépublication)

Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning

We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor… (voir plus)-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy policy gradients on control tasks, existing general methods suffer from high variance and instability, partly because the policy improvement depends on gradient of the estimated value function. In this work, we present a new way of off-policy policy evaluation in actor-critic, based on the doubly robust estimators. We extend the doubly robust estimator from off-policy policy evaluation (OPE) to actor-critic algorithms that consist of a reward estimator performance model. We find that doubly robust estimation of the critic can significantly improve performance in continuous control tasks. Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.

2019-12-09

arXiv (prépublication)