The Impact of Determinism on Learning Atari 2600 Games

By Matthew J. Hausknecht, Peter Stone. 2015

38 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (2017, background): “…many researchers have augmented the standard behavior of the ALE to ev luat the robustness of their agents and to discourage memorization (e.g., random frame skips, Brockman et al., 2016; injecting stochasticity, Hausknecht & Stone, 2015; no-ops, Mnih et al., 2015; human starts, Nair t l., 2015).”
Google Research Football: A Novel Reinforcement Learning Environment (2019, background): “…techniques have been developed to add artiﬁcial randomness to the environment (like skipping a random number of initial frames or using sticky actions), this randomness may still be too structured and easy to predict and incorporate during training (Machado et al. 2018; Hausknecht and Stone 2015).”
A Study on Overfitting in Deep Reinforcement Learning (2018, background): “Stochastic policy Hausknecht & Stone (2015) Random starts Mnih et al. (2015); Hausknecht & Stone (2015); Nair et al. (2015) Sticky actions Hausknecht & Stone (2015); Brockman et al. (2016); Machado et al. (2017) Frame skipping Brockman et al. (2016) The formal setup of RL problems are usually in…”
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis (2018, unknown): “In contrast, Atari games are deterministic [42].”
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning (2019, background): “Finally, we also evaluate on a stochastic variant of the popular ALE framework consisting of Atari games (Machado et al., 2017a).”