From simplified environments to Atari Breakout
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A reinforcement-learning study progressing from Q-learning in a simplified brick game to DQN in Atari Breakout, with implementation, debugging and training observations.
Main observations
The report contrasts tabular and deep value methods across different state spaces and retains demonstration and source-code references.
Scope and limitations
The simplified task and Atari use different state representations and conditions, so their scores are not directly comparable evaluations.
Source
View the original source and its recorded versionPreserved as a historical record. The report describes the software, conditions and observations of its original experiment.