SalahEddineGhamri

local function greet_function(opts)
    local name = opts.args or "World"
    print("Hello, " .. name .. "!")
end

vim.api.nvim_create_user_command('Greet', greet_function, {
    nargs = '?',  -- Optional argument
    desc = "Greet someone"  -- Description for :help
})

SalahEddineGhamri

-- Define your function
local function my_function()
    print("Hello from my function!")
    -- Your code here
end

-- Create a user command
vim.api.nvim_create_user_command('MyCommand', my_function, {})

SalahEddineGhamri

"""
Simple Q learning algorithm with epsilon-greedy action choice

in q-learning algorithm we maintain a table of stats-actions q-values
after attributing the rewards to the goal states
the table is update based on bellman learning formula
to propagate the q values on the state-action back to the start

learning is done through episodes
in the end the result path is calculated from the learned policy