def main():
print "Creating DQN agent..."
# env = gym.make("codegen-v0")
set_debugger_org_frc()
iters = 6300
n_goal = 0
n_goal_all = 0
time_stamp = 0
max_steps = 5
agent = DQNAgent(max_steps)
agent.dqn.initial_exploration = 6000 * max_steps
for iter in range(iters):
print "\n********Iteration # ", iter, "***********\n"
# 1 iteration
env = gym.make("codegen-v0")
num = random.randrange(1, 100)
print "Goal Number : ", num + 1
env.my_input = num
#env.goal = "['" + env.my_input + "']"
env.goal = str(num + 1)
code = env._reset()
step_in_episode = 0
total_score = 0.0
reward = 0.0
mystate = []
my_state_new = []
# debug : the sys
# sss = []
# for arg in sys.argv[1:]:
# sss.append(arg)
# print "sss = " , sss
# while True:
while step_in_episode < max_steps:
# state = env.code_index_list + [-1]*(max_steps-len(env.code_index_list
state = env.code_index_list[:]
state += np.zeros([
max_steps - len(env.code_index_list), agent.dqn.code_idx_size
],
dtype=int).tolist()
# state = state.tolist()
# state = 1;
# print "env = ",env.code_index_list
# print "state = ",state
# raw_input()
if step_in_episode == 0:
action_idx = agent.start(code, state)
else:
action_idx = agent.act(code, state, reward)
code, reward, terminal, info = env._step(action_idx,
agent.dqn.actions)
state_prime = env.code_index_list[:]
state_prime += np.zeros([
max_steps - len(env.code_index_list), agent.dqn.code_idx_size
],
dtype=int).tolist()
# debug : the sys
# sss = []
# for arg in sys.argv[1:]:
# sss.append(arg)
# print "sss = " , sss
print "state : "
print state
print "state' : "
print state_prime
if step_in_episode == max_steps - 1:
agent.dqn.stock_experience(agent.dqn.time_stamp, state,
action_idx, reward, state_prime, 1)
else:
agent.dqn.stock_experience(agent.dqn.time_stamp, state,
action_idx, reward, state_prime, 0)
agent.dqn.experience_replay(agent.dqn.time_stamp)
agent.dqn.target_model_update(agent.dqn.time_stamp,
soft_update=False)
total_score += reward
if terminal:
agent.dqn.goal_idx.append(agent.dqn.time_stamp)
agent.end(reward)
agent.dqn.stock_experience(agent.dqn.time_stamp, state,
action_idx, reward, state_prime, 1)
n_goal_all += 1
step_in_episode += 1
agent.dqn.time_stamp += 1
if iters - iter <= 100:
n_goal += 1
break
step_in_episode += 1
agent.dqn.time_stamp += 1
if iter == 1 + (agent.dqn.initial_exploration / max_steps):
print "n_goal_all = ", n_goal_all
print agent.dqn.goal_idx
raw_input()
print "n_goal : ", n_goal
print "epsilon : ", agent.epsilon
def main():
print "Creating DQN agent..."
iters = 10000
n_goal = 0
n_goal_all = 0
time_stamp = 0
############################################################
# print x
# max_steps = 3
# actions = ["print", " ", "x"]
############################################################
############################################################
# print x+1
max_steps = 5
actions = ["print", " ", "x", "+", "1"]
############################################################
agent = DQNAgent(max_steps, actions)
agent.dqn.initial_exploration = iters * 0.6
results = []
policy_frozen = False
wins_file = "wins.txt"
with io.FileIO(wins_file, "w") as file:
file.write("Winning codes:\n")
for iter in range(iters):
print "\n\n::{}::".format(iter)
if iter == 4300: # 2300:
policy_frozen = True
env = gym.make("codegen-v0")
num = random.randrange(1, 100)
env.my_input = num
############################################################
# print x
# env.goal = str(num)
############################################################
############################################################
# print x+1
env.goal = str(num + 1)
############################################################
code = env._reset()
step_in_episode = 0
total_score = 0.0
reward = 0.0
mystate = []
my_state_new = []
while step_in_episode < max_steps:
state = env.code_index_list[:]
state += np.zeros([
max_steps - len(env.code_index_list), agent.dqn.code_idx_size
],
dtype=int).tolist()
if step_in_episode == 0:
action_idx = agent.start(code, state, policy_frozen)
else:
action_idx = agent.act(code, state, reward)
code, reward, terminal, info = env._step(action_idx,
agent.dqn.actions)
state_prime = env.code_index_list[:]
state_prime += np.zeros([
max_steps - len(env.code_index_list), agent.dqn.code_idx_size
],
dtype=int).tolist()
agent.dqn.experience_replay(agent.dqn.time_stamp)
if step_in_episode == max_steps - 1 or terminal:
agent.dqn.stock_experience(agent.dqn.time_stamp, state,
action_idx, reward, state_prime,
True)
if terminal:
agent.dqn.goal_idx.append(agent.dqn.time_stamp)
agent.dqn.time_stamp += 1
else:
agent.dqn.stock_experience(agent.dqn.time_stamp, state,
action_idx, reward, state_prime,
False)
total_score += reward
if terminal:
agent.end(reward)
n_goal_all += 1
step_in_episode += 1
if iters - iter <= 100:
n_goal += 1
step_in_episode += 1
if iter >= 100:
results = results[1:]
if reward >= 1:
print "WIN"
results.append(1.0)
with io.FileIO(wins_file, "a") as f:
f.write(
"\n=====================\n{}\n=====================\n\n".
format(code))
f.flush()
os.fsync(f)
else:
results.append(0.0)
total_iters = 100 if iter >= 100 else iter + 1
print "TOTAL {:.2f}% of wins in last {} iters, sum: {}, total good: {}".format(
100 * sum(results) / total_iters, total_iters, sum(results),
len(agent.dqn.goal_idx))
if iter == 1 + agent.dqn.initial_exploration:
print "n_goal_all = ", n_goal_all
print agent.dqn.goal_idx
raw_input()
print "n_goal : ", n_goal
print "epsilon : ", agent.epsilon
