Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments

12 Apr 2019Kaleigh ClaryEmma ToschJohn FoleyDavid Jensen

Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment. Unfortunately, there are still pernicious sources of variability in reinforcement learning agents that make reporting common summary statistics an unsound metric for performance... (read more)

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