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Gyx allows designing and training Reinforcement Learning tasks. It includes environment abstractions that allows interaction with Python based environments like OpenAI Gym.

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gyx lib supervisor.ex
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lib/supervisor.ex

defmodule Gyx.Supervisor do
@moduledoc false
use Supervisor
def start_link do
Supervisor.start_link(__MODULE__, [])
end
def init(_args) do
supervise(children(), strategy: :one_for_one)
end
defp children do
[
#worker(Gyx.Environments.Pure.Blackjack, [[], [name: Gyx.Environments.Pure.Blackjack]])
#worker(Gyx.Qstorage.QGenServer, [[], [name: Gyx.Qstorage.QGenServer]]),
#worker(Gyx.Environments.Gym, [[], [name: Gyx.Environments.Gym]]),
#worker(Gyx.Agents.SARSA.Agent, [[], [name: Gyx.Agents.SARSA.Agent]]),
#worker(Gyx.Trainers.TrainerSarsa, [[], [name: Gyx.Trainers.TrainerSarsa]]),
#worker(Gyx.Experience.ReplayBufferETS, [[], [name: Gyx.Experience.ReplayBufferETS]])
]
end
end