FSP.py

import FSP_algo
import games.leduc as leduc
import games.kuhn as KP
import matplotlib.pyplot as plt
import agents.learners as learners
from UI.plot_funcs import FSP_plots 
import numpy as np

def run(opts):
    #sort of working
    extras = 20
    num_BR = 30
    num_mixed = 10
    iters= 200000
    time = 300
    
    #test
    extras = 0
    num_BR = 30000
    num_mixed = 20000
    iters = 100000000
    time = 36000
    RL_iters = 1
    check_freq = 1
    
    res = []
    iters = opts["num_lvls"]
    game_obj = opts["game"]
    #new test
    #extras = 0
    #Num_BR = 3000
    #Num_mixed = 2000
    #Iters = 10000000
    #RL_iters = 1000
    #Time = 300
    #pol = np.array([[1/3,2/3],[0,1],[1,0],[0,1],[1/3,2/3],[1,0]])
    #pol = np.ones((6,2))/2
    #exact = learners.kuhn_exact_solver(pol,1)
    #import pdb; pdb.set_trace()
    #game_obj = leduc.leduc_int()
    #game_obj = KP.Kuhn_Poker_int_io()
    
    RL_learners = [learners.actor_critic(learners.softmax, learners.value_advantage, game_obj.num_actions[p],\
                    game_obj.num_states[p], init_adv=0, extra_samples = extras, tol=1e-5)\
                   for p in range(2)]
    #RL_learners = [learners.fitted_Q_iteration(0, (game_obj.num_states[p], game_obj.num_actions[p])) for p in range(2)] 
    SL_learners = [learners.count_based_SL((game_obj.num_states[p], game_obj.num_actions[p])) for p in range(2)]
    
    agents = [learners.complete_learner(RL_learners[p], SL_learners[p], num_loops = RL_iters) for p in range(2)]
    
    worker = FSP_algo.FSP(game_obj, agents, max_iters=iters, max_time=time, m=num_BR, n=num_mixed, exploit_freq=check_freq)
    pi, exploitability, data = worker.run_algo()
    
    #FSP_plots(exploitability, worker.est_exploit_freq, [pi], 'kuhn') 
    res.append({"exploit":exploitability, "extra_data":data})
    return res
    #np.savez(filename, pi=pi, exploit=exploitability, extra_data=data)