def main():
winners = {}
winners["1"] = 0
winners["-1"] = 0 # Collecting stats of the games
nGames = 1000 # how many games?
arr = np.zeros(nGames)
for g in range(nGames):
winner = Backgammon.play_a_game(commentary=False, net=new_agent)
# print("game %i finished", g)
# print("winner is ", winner)
winners[str(winner)] += 1
arr[g] = winner
# print("this is hw")
# print(new_agent.val_func_nn.hidden_weights)
# print("this is theta")
# print(new_agent.policy_nn.theta)
if (g % 50 == 0):
print(new_agent.torch_nn_policy.theta)
# print(winners)
file = open('Failed.py', 'w')
file.write(np.array_str(arr))
file.close()
print("Out of", nGames, "games,")
print("player", 1, "won", winners["1"], "times and")
print("player", -1, "won", winners["-1"], "times")
def main():
winners = {}
winners["1"] = 0
winners["-1"] = 0 # Collecting stats of the games
nGames = 10000 # how many games?
arr = np.zeros(nGames)
for g in tqdm(range(nGames)):
# w=new_agent.actor.theta
# print(w)
###Zero eligibility traces (according to psudo code)
agent.actor.zero_el()
agent.critic.zero_el()
winner = Backgammon.play_a_game(commentary=False, net=agent)
winners[str(winner)] += 1
arr[g] = winner
# if(g % 100 == 0):
# print(new_agent.torch_nn_policy.theta)
# print(winners)
#
##Save the agent
file_net = open('saved_net_one_2', 'wb')
pickle.dump(agent, file_net)
file_net.close()
print("Out of", nGames, "games,")
print("player", 1, "won", winners["1"], "times and")
print("player", -1, "won", winners["-1"], "times")
def main():
ranges = 1
winners = {}
winners["1"] = 0
winners["-1"] = 0 # Collecting stats of the games
nGames = 1000 # how many games?
arr = np.zeros(nGames)
for g in tqdm(range(nGames)):
# ##Zero eligibility traces (according to psudo code)
winner = Backgammon.play_a_game(commentary=False, net=agent, train=train)
winners[str(winner)] += 1
arr[g] = winner
if(g % 10 == 0):
print(agent.actor.theta)
k = winners["1"]
print("winrate is %f" % (k / (g + 0.00000001)))
# print(winners)
# Save the agent
if(train is True):
file_net = open('saved_net_one', 'wb')
pickle.dump(agent.actor.theta, file_net)
file_net.close()
print("Out of", ranges, nGames, "games,")
print("player", 1, "won", winners["1"], "times and")
print("player", -1, "won", winners["-1"], "times")
def evaluate(agent, evaluation_agent, n_eval, n_games):
wins = 0
for i in range(n_eval):
winner, board = Backgammon.play_a_game(agent, evaluation_agent)
wins += int(winner == 1)
winrate = round(wins / n_eval * 100, 3)
print("Win-rate after training for " + str(n_games) + " games: " +
str(winrate) + "%")
return winrate
for i in range(n_eval):
winner, board = Backgammon.play_a_game(agent, evaluation_agent)
wins += int(winner == 1)
winrate = round(wins / n_eval * 100, 3)
print("Win-rate after training for " + str(n_games) + " games: " +
str(winrate) + "%")
return winrate
def train(n_games=200_000, n_epochs=5000, n_eval=1000):
agent = kotra
evaluation_agent = pubeval
winrates = []
for g in range(n_games):
if g % n_epochs == 0 and g != 0:
winrate = evaluate(agent, evaluation_agent, n_eval, n_games=g)
winrates.append(winrate)
winner, board = Backgammon.play_a_game(agent,
agent,
train=True,
train_config={'g': g})
agent.game_over_update(board, int(winner == 1))
agent.game_over_update(kotra.flip_board(board), int(winner == -1))
plot_perf(winrates)
# ----- main -----
train()