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examples.py
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examples.py
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# Quickstart
import numpy as np
from ramo.game.example_games import get_monfg
from ramo.utility_function.functions import get_u
from ramo.strategy.best_response import calc_best_response
game = get_monfg('game1') # Get a predefined game.
u = get_u('u1') # Get a utility function.
player = 0 # Player zero.
payoff_matrix = game.get_payoff_matrix(player) # Get this player's payoff matrix.
player_strategy = np.array([0, 0, 1]) # This strategy will be optimised. The starting point does actually not matter.
opponent_strategy = np.array([1, 0, 0]) # Define an opponent strategy, here a pure strategy playing only action 1.
joint_strategy = [player_strategy, opponent_strategy] # A joint strategy.
best_response = calc_best_response(u, player, payoff_matrix, joint_strategy)
print(best_response)
# Example 1: Solving a game
from ramo.game.example_games import get_monfg
from ramo.utility_function.functions import get_u
game = get_monfg('game1') # Get a predefined game.
u1, u2 = get_u('u1'), get_u('u1') # Get a utility function.
##########
import ramo.printing as pt
pt.print_monfg(game, 'Game 1')
##########
from ramo.nash.moqups import moqups
u_tpl = (u1, u2)
psne = moqups(game, u_tpl)
print(psne)
##########
from ramo.strategy.operations import make_profile_from_pure_joint_strat
action_profiles = [make_profile_from_pure_joint_strat(joint_strat) for joint_strat in psne]
pt.print_monfg(game, 'Game 1', highlight_cells=action_profiles)
##########
from ramo.nash.fictitious_play import fictitious_play
from ramo.nash.IBR import iterated_best_response
ne_fp = fictitious_play(game, u_tpl)
ne_ibr = iterated_best_response(game, u_tpl)
# Example 2: Running baseline algorithms
from ramo.game.generators import discrete_uniform_monfg
from ramo.utility_function.functions import get_u
game = discrete_uniform_monfg(player_actions=(3, 3), num_objectives=2, reward_min_bound=0, reward_max_bound=5)
u1, u2 = get_u('u1'), get_u('u1')
u_tpl = (u1, u2)
##########
from ramo.learner.execute_learner import execute_learner
experiment = 'indep_ac' # Independent actor critic.
runs = 2
episodes = 10
rollouts = 10
data = execute_learner(game, u_tpl, experiment=experiment, runs=runs, episodes=episodes, rollouts=rollouts)
returns_log, action_probs_log, state_dist_log, metadata = data
##########
from ramo.commitment.execute_commitment import execute_commitment
experiment = 'non_stationary'
runs = 2
episodes = 10
rollouts = 10
alternate = False
data = execute_commitment(game, u_tpl, experiment=experiment, runs=runs, episodes=episodes, rollouts=rollouts,
alternate=alternate)
returns_log, action_probs_log, state_dist_log, com_probs_log, metadata = data
# Example 3: Hypothesis testing
def u1(vec):
x, y = vec
return x ** 2 + y
def u2(vec):
x, y = vec
return x ** 2 + x * y + y ** 2
u_tpl = (u1, u2)
##########
from sympy.abc import x, y
from ramo.utility_function.checking import is_convex, is_strictly_convex
symb_u1 = x ** 2 + y
res1 = is_convex(symb_u1)
print(res1)
symb_u2 = x ** 2 + x * y + y ** 2
res2 = is_strictly_convex(symb_u2)
print(res2)
##########
res3 = is_strictly_convex(symb_u1)
print(res3)
##########
import numpy as np
from ramo.game.monfg import MONFG
from ramo.game.checking import is_degenerate_pure
payoffs = [np.array([[(1, 2), (2, 1)],
[(1, 2), (1, 2)]], dtype=float),
np.array([[(1, 2), (2, 1)],
[(2, 1), (1, 2)]], dtype=float)]
monfg = MONFG(payoffs)
res = is_degenerate_pure(monfg)
print(res)
##########
payoffs = [np.array([[(1, 2), (2, 1)],
[(2, 1), (1, 2)]], dtype=float),
np.array([[(1, 2), (2, 1)],
[(2, 1), (1, 2)]], dtype=float)]
monfg = MONFG(payoffs)
res = is_degenerate_pure(monfg)
print(res)
##########
from ramo.nash.moqups import moqups
psne = moqups(monfg, u_tpl)
print(psne)
##########
from ramo.printing import print_monfg
from ramo.strategy.operations import make_profile_from_pure_joint_strat
action_profiles = [make_profile_from_pure_joint_strat(ne) for ne in psne]
print_monfg(monfg, 'Special Game', action_profiles)
##########
strat1 = np.array([0.5, 0.5])
strat2 = np.array([0.5, 0.5])
joint_strat = [strat1, strat2]
##########
from ramo.strategy.best_response import calc_expected_returns
exp1 = calc_expected_returns(0, monfg.payoffs[0], joint_strat)
print(exp1)
exp2 = calc_expected_returns(1, monfg.payoffs[1], joint_strat)
print(exp2)
##########
from ramo.nash.verify import verify_nash
is_ne = verify_nash(monfg, u_tpl, joint_strat)
print(is_ne)