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Scott Fujimoto

fujimots@mila.quebec
PhD - McGill University
Supervisor
Doina Precup
Co-supervisor
David Meger

Publications

Imitation Learning from Observation through Optimal Transport
Wei-Di Chang
Scott Fujimoto
David Meger
Gregory Dudek
2023-10-02
ArXiv (preprint)
doi.org
arxiv.org
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto
Wei-Di Chang
Edward J. Smith
Shixiang Shane Gu
Doina Precup
David Meger
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked… (see more) for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.
openreview.net