Deep Policy Gradient Algorithms: A Closer Look

Deep reinforcement learning methods are behind some of the most publicized recent results in machine learning. In spite of these successes, however, deep RL methods face a number of systemic issues: brittleness to small changes in hyperparameters, high reward variance across runs, and sensitivity to seemingly small algorithmic changes.

In this talk we take a closer look at the potential root of these issues. Specifically, we study how the policy gradient primitives underlying popular deep RL algorithms reflect the principles informing their development.

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Series:
Microsoft Research Talks
Date:
Speakers:
Logan Engstrom
Affiliation:
MIT CSAIL

Series: Microsoft Research Talks