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Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
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.
On Bonus-Based Exploration Methods in the Arcade Learning Environment
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).
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE).
The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes.
Approximate Exploration through State Abstraction
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.
