Introduces replicable random design regression and covariance estimation tools to enable the first provably efficient replicable RL algorithms for linear MDPs in generative and episodic settings.
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
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. Our experiments demonstrate the variability of common agents used in the popular OpenAI Baselines repository. We make the case for reporting post-training agent performance as a distribution, rather than a point estimate.
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UNVERDICTED 3representative citing papers
A 2084-parameter recurrent policy trained by distilling 1000 RL teacher policies enables zero-shot control across 10 real quadrotors differing in mass, motors, frames, propellers, and flight controllers.
Proposes min-max IPR and percentile highlighting to evaluate run-to-run performance variation in deep RL, with case studies on normalizations in PPO/SAC, algorithm comparisons, and DQN/Rainbow on Atari.
citing papers explorer
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Performance Variation in Deep Reinforcement Learning
Proposes min-max IPR and percentile highlighting to evaluate run-to-run performance variation in deep RL, with case studies on normalizations in PPO/SAC, algorithm comparisons, and DQN/Rainbow on Atari.