Search Results for author: Adrien Ali Taïga

Found 5 papers, 0 papers with code

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

no code implementations6 Mar 2024 Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions.

Atari Games regression +1

On Bonus-Based Exploration Methods in the Arcade Learning Environment

no code implementations22 Sep 2021 Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016).

Montezuma's Revenge

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

no code implementations6 Aug 2019 Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE).

Benchmarking Montezuma's Revenge

The Value Function Polytope in Reinforcement Learning

no code implementations31 Jan 2019 Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes.

reinforcement-learning Reinforcement Learning (RL)

Approximate Exploration through State Abstraction

no code implementations29 Aug 2018 Adrien Ali Taïga, Aaron Courville, Marc G. Bellemare

Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration.

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