thomasahle

import collections
from ortools.linear_solver import pywraplp

# xy format
tetras = [
        # Up downs
        ((0,0),(0,1),(1,1),(1,2)),
        ((0,0),(0,1),(-1,1),(-1,2)),
        # left rights
        ((0,0),(1,0),(1,-1),(2,-1)),

thomasahle

class ParameterDeque(nn.Module):
    def __init__(self) -> None:
        super(ParameterDeque, self).__init__()
        self.left = 0
        self.right = 0 # Points at the first non-existing element

    def _convert_idx(self, idx):
        """Get the absolute index for the list of modules"""
        idx = operator.index(idx)

thomasahle

def play(r1, r2, replay_buffer):
    privs = [game.make_priv(r1, 0).to(device),
             game.make_priv(r2, 1).to(device)]

    def play_inner(state):
        cur = game.get_cur(state)
        calls = game.get_calls(state)
        assert cur == len(calls) % 2

        if calls and calls[-1] == game.LIE_ACTION:

thomasahle

def update(state, t):
  pi = compute_policy(state, t)
  score = 0
  for i in actions(state):
    score_i = update(state + action)
    score += pi[i] * score_i
  state.mean_score = (state.mean_score * t + score)/(t + 1)
  return score

def compute_policy(state):

thomasahle

def update(state, t):
  pi = compute_policy(state, t)
  score = 0
  for i in actions(state):
    score_i = update(state + i)
    score += pi[i] * score_i
  state.mean_score = (state.mean_score * t + score)/(t + 1)
  return score

def compute_policy(state):

thomasahle

def play(r1, r2, replay_buffer):
    privs = [game.make_priv(r1, 0).to(device),
            game.make_priv(r2, 1).to(device)]

    def play_inner(state):
        cur = game.get_cur(state)
        calls = game.get_calls(state)
        assert cur == len(calls) % 2

        if calls and calls[-1] == game.LIE_ACTION:

thomasahle

def play(r1, r2, replay_buffer):
    privs = [game.make_priv(r1, 0), game.make_priv(r2, 1)]

    def play_inner(state):
        cur = game.get_cur(state)
        calls = game.get_calls(state)
        assert cur == len(calls) % 2

        if calls and calls[-1] == game.LIE_ACTION:
            prev_call = calls[-2] if len(calls) >= 2 else -1

thomasahle

def play(r1, r2, replay_buffer):
    privs = [game.make_priv(r1, 0), game.make_priv(r2, 1)]

    def play_inner(state):
        cur = game.get_cur(state)     # Current player id
        calls = game.get_calls(state) # Bets made by player so far

        if calls and calls[-1] == game.LIE_ACTION:
            prev_call = calls[-2] if len(calls) >= 2 else -1
            # If prev_call is good it mean we won (because our opponent called lie)

thomasahle

[nav] In [48]: table = torch.arange(12, dtype=torch.float32).reshape(4,3)

[ins] In [49]: new_table = torch.zeros(4, 3)

[ins] In [50]: index = torch.tensor([1,1,0,3])

[ins] In [51]: index2 = index.unsqueeze(1).expand(4,3)

[ins] In [52]: table
Out[52]:

thomasahle

import torch.nn as nn

class TestModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.a = nn.Linear(10, 10)
        self.b = SubTestModule()

class SubTestModule(nn.Module):
    def __init__(self):