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you might want to think about trying to train your algorithm on an environment, which generates a new, solvable room for every game. This ensures that your model is not learning the limited number of solutions for the predefined rooms, as pointed out in Imagination-Augmented Agents for Deep Reinforcement Learning . I developed an environment for OpenAi gym, which does exactly that. https://github.com/mpSchrader/gym-sokoban
Best,
Max
The text was updated successfully, but these errors were encountered:
Hi Ruben,
you might want to think about trying to train your algorithm on an environment, which generates a new, solvable room for every game. This ensures that your model is not learning the limited number of solutions for the predefined rooms, as pointed out in Imagination-Augmented Agents for Deep Reinforcement Learning
. I developed an environment for OpenAi gym, which does exactly that.
https://github.com/mpSchrader/gym-sokoban
Best,
Max
The text was updated successfully, but these errors were encountered: