After I got the ‘gamenev’ API working, it became very easy to program an agent/player which picks complete random actions (with equal probabilities for all possible moves). As shown in the source code test/test_gameenv.lua, all that’s required is to call gameenv.new_game() once, followed by gameenv.step(a) repeatedly until the game is over.

 for i = 1, opt.games do
     local terminal = false
     local cnt = 0
     gameenv.new_game()
     while not terminal do
         cnt = cnt + 1
         if cnt % opt.actstep == 0 then
             -- take a random action
             _, _, terminal = gameenv.step(actions[math.random(#actions)])
         else
             _, _, terminal = gameenv.step()
        end
     end
     gameenv.step(0)  -- release all buttons
     print(('Game #%3d, score = '):format(i) .. gameenv.get_score())
     history[#history + 1] = gameenv.get_score()
 end

I experimented with different opt.actstep values (repeat same action for this many consecutive steps/frames before trying to take another action). The result is shown in the table below: average, minimum, maximum scores over 100 games for the 6 different configurations. As it turned out, the random player with opt.actstep = 2 got the highest average score: 6314.3. So that would be the score to beat for my DQN AI agent!

I also made some histograms for comparison: opt.actstep = 1~4 over 100 games each.