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Scott Fujimoto

Alumni

Publications

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto
David Meger
Doina Precup
Ofir Nachum
Shixiang Shane Gu
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is… (voir plus) uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the accuracy of the value function. In particular, we show that (1) due to cancellations from both sides of the Bellman equation, the magnitude of the Bellman error is only weakly related to the distance to the true value function, even when considering all state-action pairs, and (2) in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This means that the Bellman error can be minimized without improving the accuracy of the value function. We demonstrate these phenomena through a series of propositions, illustrative toy examples, and empirical analysis in standard benchmark domains.
2022-06-27
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang
Juan Higuera
Scott Fujimoto
David Meger
Gregory Dudek
2022-05-18
ArXiv (prépublication)
doi.org
arxiv.org
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto
David Meger
Doina Precup
Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a… (voir plus) sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.
2021-06-30
Proceedings of the 38th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Scott Fujimoto
David Meger
Doina Precup
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (voir plus)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
doi.org
arxiv.org
GeoMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Edward J. Smith
Scott Fujimoto
Adriana Romero
David Meger
Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distrib… (voir plus)uted vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue that the graph representation of geometric objects allows for additional structure, which should be leveraged for enhanced reconstruction. Thus, we propose a system which properly benefits from the advantages of the geometric structure of graph encoded objects by introducing (1) a graph convolutional update preserving vertex information; (2) an adaptive splitting heuristic allowing detail to emerge; and (3) a training objective operating both on the local surfaces defined by vertices as well as the global structure defined by the mesh. Our proposed method is evaluated on the task of 3D object reconstruction from images with the ShapeNet dataset, where we demonstrate state of the art performance, both visually and numerically, while having far smaller space requirements by generating adaptive meshes
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto
David Meger
Doina Precup
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, … (voir plus)without offering further possibility for data collection. In this paper, we demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are incapable of learning with data uncorrelated to the distribution under the current policy, making them ineffective for this fixed batch setting. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data. We present the first continuous control deep reinforcement learning algorithm which can learn effectively from arbitrary, fixed batch data, and empirically demonstrate the quality of its behavior in several tasks.
2019-05-23
Proceedings of the 36th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Where Off-Policy Deep Reinforcement Learning Fails
Scott Fujimoto
David Meger
Doina Precup
This work examines batch reinforcement learning–the task of maximally exploiting a given batch of off-policy data, without further data co… (voir plus)llection. We demonstrate that due to errors introduced by extrapolation, standard off-policy deep reinforcement learning algorithms, such as DQN and DDPG, are only capable of learning with data correlated to their current policy, making them ineffective for most off-policy applications. We introduce a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space to force the agent towards behaving on-policy with respect to a subset of the given data. We extend this notion to deep reinforcement learning, and to the best of our knowledge, present the first continuous control deep reinforcement learning algorithm which can learn effectively from uncorrelated off-policy data.
2018-09-26
(publié)
openreview.net
openreview.net
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto
Herke van Hoof
David Meger
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated valu… (voir plus)e estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.
2018-07-02
Proceedings of the 35th International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press