45 citations · 194 across the 38 of their papers we have counts for
17 papers · 1 filter
Efficient Imitation under Misspecification
Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun +1
We consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true…
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
Gokul Swamy, Sanjiban Choudhury, Wen Sun +2
From a first-principles perspective, it may seem odd that the strongest results in foundation model fine-tuning (FT) are achieved via a relatively complex, two-stage training proce…
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
David Wu, Sanjiban Choudhury
Existing inverse reinforcement learning methods (e.g. MaxEntIRL, -IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop. This…
Hybrid Inverse Reinforcement Learning
Juntao Ren, Gokul Swamy, Zhiwei Steven Wu +2
The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations wi…
Learning Shared Safety Constraints from Multi-task Demonstrations
Konwoo Kim, Gokul Swamy, Zuxin Liu +3
Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whe…
Inverse Reinforcement Learning without Reinforcement Learning
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunat…
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
Anirudh Vemula, Yuda Song, Aarti Singh +2
We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in…
Sequence Model Imitation Learning with Unobserved Contexts
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
We consider imitation learning problems where the learner's ability to mimic the expert increases throughout the course of an episode as more information is revealed. One example o…
Minimax Optimal Online Imitation Learning via Replay Estimation
Gokul Swamy, Nived Rajaraman, Matthew Peng +5
Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infi…
Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions. When noise affects multiple timesteps of recorded…
A Critique of Strictly Batch Imitation Learning
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model, with the hope of out-performing standa…
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching. At its core, our classification scheme is based on wheth…
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman +2
Imitation learning practitioners have often noted that conditioning policies on previous actions leads to a dramatic divergence between "held out" error and performance of the lear…
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots
Model-Predictive Control (MPC) is a powerful tool for controlling complex, real-world systems that uses a model to make predictions about future behavior. For each state encountere…
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
Gilwoo Lee, Sanjiban Choudhury, Brian Hou +1
We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP fram…