def run_epoch(self, epoch, num_steps, prefix, collect_reward=False):
""" Run one 'epoch' of training or testing, where an epoch is defined
by the number of steps executed. Prints a progress report after
every trial
Arguments:
num_steps - steps per epoch
prefix - string to print ('training' or 'testing')
"""
steps_left = num_steps
if prefix == "training" or not collect_reward:
while steps_left > 0:
print prefix + " epoch: ", epoch, "steps_left: ", steps_left
sys.stdout.flush()
terminal = RLGlue.RL_episode(steps_left)
if not terminal:
RLGlue.RL_agent_message("episode_end")
steps_left -= RLGlue.RL_num_steps()
elif prefix == "testing":
total_reward = 0
episode_counter = 0
terminal = False
while steps_left > 0:
if terminal:
print prefix + " epoch: ", epoch, "steps_left: ", steps_left
sys.stdout.flush()
roat = RLGlue.RL_step()
reward = roat.r
terminal = roat.terminal
total_reward += reward
episode_counter += terminal
steps_left -= 1
return total_reward, episode_counter
def runEpisode(is_learning_episode):
global whichEpisode, learningEpisode
RLGlue.RL_episode(10000)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
whichEpisode += 1
if is_learning_episode:
learningEpisode += 1
#print "Episode " + str(learningEpisode) + "/" + str(whichEpisode) + "\t " + str(totalSteps) + " steps \t" + str(totalReward) + " total reward\t "
print "Episode %d/%d\t %d steps \t %.1f total reward\t" % (
learningEpisode, whichEpisode, totalSteps, totalReward)
else:
#print "Evaluation ::\t " + str(totalSteps) + " steps \t" + str(totalReward) + " total reward\t "
print "Evaluation ::\t %d steps \t %.1f total reward" % (totalSteps,
totalReward)
with open('eval_dump.json', 'a') as f:
json.dump(
{
"Steps": totalSteps,
"Episode": whichEpisode,
"Reward": totalReward
}, f)
f.write('\n')
return totalSteps
def run_episode(self):
""" Run a single episode """
# Update epsilon
'''
phase_len = self.episodes / 3
if self.episode_number == phase_len * 2:
# Start low phase
RLGlue.RL_agent_message('set epsilon %f' % (self.epsilon_low))
elif self.episode_number >= phase_len and self.episode_number < phase_len * 2:
# In decr phase
epsilon = float(RLGlue.RL_agent_message('get epsilon'))
epsilon += (self.epsilon_decr - self.epsilon_high) / phase_len
RLGlue.RL_agent_message('set epsilon %f' % (epsilon))
elif self.episode_number == 0:
# Start high phase
RLGlue.RL_agent_message('set epsilon %f' % (self.epsilon_high))
'''
terminal = RLGlue.RL_episode(0) # 0 - run until terminal
steps = RLGlue.RL_num_steps()
reward = RLGlue.RL_return()
#print "\nEpisode %d\t %d steps\t reward: %d" % (self.episode_number, steps, reward)
#print "Episode "+str(episode_number)+"\t "+str(totalSteps)+ " steps \t" + str(totalReward) + " total reward\t " + str(terminal) + " natural end"
self.returns[self.episode_number] = (
reward + self.returns[self.episode_number] *
(self.instance - 1)) / self.instance
self.steps[self.episode_number] = (steps +
self.steps[self.episode_number] *
(self.instance - 1)) / self.instance
self.episode_number += 1
def runEpisode(step_limit):
global which_episode
global total_win
global total_draw
global total_lose
global ns_epoch
global pcts_win
global pcts_win_or_draw
global pcts_lose
which_episode += 1
# ゲーム1回 開始
terminal = RLGlue.RL_episode(step_limit)
# 勝負がつくまでのステップ数と報酬を取得
total_steps = RLGlue.RL_num_steps()
total_reward = RLGlue.RL_return()
# 今回の結果を表示
r_win = 1.0
r_draw = -0.5
r_lose = -1.0
if total_reward == r_win:
total_win += 1
elif total_reward == r_draw:
total_draw += 1
elif total_reward == r_lose:
total_lose += 1
print("Episode " + str(which_episode) + "\t " + str(total_steps) +
" steps \t" + str(total_reward) + " total reward\t " +
str(terminal) + " natural end")
# 100回毎に勝敗を集計
record_interval = 100
if which_episode % record_interval == 0:
line = 'Episode: {}, {} wins, {} draws, {} loses'.format(
which_episode, total_win, total_draw, total_lose)
print(
'---------------------------------------------------------------')
print(line)
print(
'---------------------------------------------------------------')
# 集計結果をファイルに出力
with open('result.txt', 'a') as f:
f.writelines(line + '\n')
ns_epoch.append(which_episode)
pcts_win.append(float(total_win) / record_interval * 100)
pcts_win_or_draw.append(
float(total_win + total_draw) / record_interval * 100)
pcts_lose.append(float(total_win) / record_interval * 100)
total_win = 0
total_draw = 0
total_lose = 0
def run_epoch(epoch, num_steps, prefix):
steps_left = num_steps
while steps_left > 0:
print prefix + " epoch: ", epoch, "steps_left: ", steps_left
terminal = RLGlue.RL_episode(steps_left)
if not terminal:
RLGlue.RL_agent_message("episode_end")
steps_left -= RLGlue.RL_num_steps()
def runEpisode(stepLimit, trial): global whichEpisode terminal=RLGlue.RL_episode(stepLimit) totalSteps=RLGlue.RL_num_steps() totalReward=RLGlue.RL_return() print "Experiment "+str(trial + 1)+"\t Episode "+str(whichEpisode)+"\t "+str(totalSteps)+ " steps \t" + str(totalReward) + " total reward\t " + str(terminal) + " natural end" whichEpisode=whichEpisode+1
def run_experiment(maxsteps=100, numeps=1):
taskSpec = RLGlue.RL_init()
for ep in range(numeps):
terminal = RLGlue.RL_episode(maxsteps)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
print "Episode " + str(ep) + "\t " + str(
totalSteps) + " steps \t" + str(
totalReward) + " total reward\t " + str(
terminal) + " natural end"
RLGlue.RL_cleanup()
def runEpisode(stepLimit):
# stepLimit of 0 implies no limit
global whichEpisode
terminal = RLGlue.RL_episode(stepLimit)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
print "Episode " + str(whichEpisode) + "\t " + str(
totalSteps) + " steps \t" + str(totalReward) + " total reward\t "
whichEpisode = whichEpisode + 1
def runEpisode(stepLimit):
global whichEpisode
RLGlue.RL_agent_message('reset')
terminal = RLGlue.RL_episode(stepLimit)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
print("Episode " + str(whichEpisode)),
print("\t " + str(totalSteps)),
print(" steps \t" + str(totalReward)),
print " total reward\t " + str(terminal) + " natural end"
RLGlue.RL_agent_message('episode_end')
whichEpisode = whichEpisode + 1
def run_episode(self): """ Run a single episode """ terminal = RLGlue.RL_episode(10) steps = RLGlue.RL_num_steps() reward = RLGlue.RL_return() #print "Episode %d\t %d steps\t reward: %d" % (episode_number, steps, reward) #print "Episode "+str(episode_number)+"\t "+str(totalSteps)+ " steps \t" + str(totalReward) + " total reward\t " + str(terminal) + " natural end" self.total_reward += reward # Update average x = self.total_reward / (self.episode_number + 1) self.results[self.episode_number] += (x - self.results[self.episode_number]) / self.instance self.episode_number += 1
def runEpisode(is_learning_episode):
global whichEpisode, learningEpisode
RLGlue.RL_episode(0)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
whichEpisode += 1
if is_learning_episode:
learningEpisode += 1
print("Episode " + str(learningEpisode) + "\t " + str(totalSteps) +
" steps \t" + str(totalReward) + " total reward\t ")
else:
print("Evaluation ::\t " + str(totalSteps) + " steps \t" +
str(totalReward) + " total reward\t ")
def run_epoch(epoch, num_steps, prefix):
""" Run one 'epoch' of training or testing, where an epoch is defined
by the number of steps executed. Prints a progress report after
every trial
Arguments:
num_steps - steps per epoch
prefix - string to print ('training' or 'testing')
"""
steps_left = num_steps
while steps_left > 0:
print prefix + " epoch: ", epoch, "steps_left: ", steps_left
terminal = RLGlue.RL_episode(steps_left)
if not terminal:
RLGlue.RL_agent_message("episode_end")
steps_left -= RLGlue.RL_num_steps()
def runEpisode(is_learning_episode):
global whichEpisode, learningEpisode
RLGlue.RL_episode(0)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
whichEpisode += 1
if is_learning_episode:
learningEpisode += 1
print "Episode " + str(learningEpisode) + "\t " + str(totalSteps) + " steps \t" + str(totalReward) + " total reward\t "
else:
print "Evaluation ::\t " + str(totalSteps) + " steps \t" + str(totalReward) + " total reward\t "
# write reward in csv file
list_csv = [str(learningEpisode), str(totalReward)]
f_csv = open('reward.csv', 'a')
writer_r = csv.writer(f_csv, lineterminator = '\n')
writer_r.writerow(list_csv)
f_csv.close()
def runEpisode(is_learning_episode):
global whichEpisode, learningEpisode
RLGlue.RL_episode(0)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
whichEpisode += 1
if is_learning_episode:
learningEpisode += 1
logger.info("{},{},{},{}".format(
dt.now().strftime("%Y-%m-%d_%H:%M:%S"), learningEpisode,
totalSteps, totalReward))
print "Episode " + str(learningEpisode) + "\t " + str(
totalSteps) + " steps \t" + str(
totalReward) + " total reward\t " + dt.now().strftime(
"%Y%m%d_%H%M%S")
else:
print "Evaluation ::\t " + str(totalSteps) + " steps \t" + str(
totalReward) + " total reward\t "
def main():
whichTrainingMDP = 1
# Uncomment ONE of the following lines to choose your experiment
#loadTetris(whichTrainingMDP); #put the desired parameter set in where MDP is in [0,19]
#loadHelicopter(whichTrainingMDP); #put the desired parameter set in where MDP is in [0,9]
#loadAcrobot(whichTrainingMDP); #put the desired parameter set in where MDP is in [1,49] #0 is standard acrobot
#loadPolyathlon(whichTrainingMDP); #put the desired parameter set in where MDP is in [0,5]
loadMario(True, True, 121, 0, 99, whichTrainingMDP)
# and then,
# just run the experiment:
RLGlue.RL_init()
episodesToRun = 10
totalSteps = 0
for i in range(episodesToRun):
RLGlue.RL_episode(20000)
thisSteps = RLGlue.RL_num_steps()
print "Total steps in episode %d is %d" % (i, thisSteps)
totalSteps += thisSteps
print "Total steps : %d\n" % (totalSteps)
RLGlue.RL_cleanup()
def run_episode(training=True): global total_episode, learned_episode, total_time, learned_steps, csv_episode, highscore, num_finished_eval_episode, evaluation_scores start_time = time.time() RLGlue.RL_episode(0) num_steps = RLGlue.RL_num_steps() total_reward = RLGlue.RL_return() total_episode += 1 elapsed_time = time.time() - start_time total_time += elapsed_time epoch = int(learned_steps / time_steps_per_epoch) if training: learned_steps += num_steps learned_episode += 1 sec = int(elapsed_time) total_minutes = int(total_time / 60) csv_episode.append([learned_episode, total_reward, num_steps, sec, total_minutes, epoch, learned_steps]) if total_reward > highscore: highscore = total_reward csv_training_highscore.append([learned_episode, highscore, total_minutes, epoch]) print "Episode:", learned_episode, "epoch:", epoch, "num_steps:", num_steps, "total_reward:", total_reward, "time:", sec, "sec", "total_time:", total_minutes, "min" return num_steps, total_reward
# $Revision$
# $Date$
# $Author$
# $HeadURL$
import sys
import rlglue.RLGlue as RLGlue
from glue_test import glue_test
tester = glue_test("test_rl_episode")
task_spec = RLGlue.RL_init()
isTerminal = RLGlue.RL_episode(0)
tester.check_fail(isTerminal != 1)
tester.check_fail(RLGlue.RL_num_steps() != 5)
isTerminal = RLGlue.RL_episode(1)
tester.check_fail(isTerminal != 0)
tester.check_fail(RLGlue.RL_num_steps() != 1)
isTerminal = RLGlue.RL_episode(2)
tester.check_fail(isTerminal != 0)
tester.check_fail(RLGlue.RL_num_steps() != 2)
isTerminal = RLGlue.RL_episode(4)
tester.check_fail(isTerminal != 0)
tester.check_fail(RLGlue.RL_num_steps() != 4)
isTerminal = RLGlue.RL_episode(5)
tester.check_fail(isTerminal != 0)
"""
Manual experiment for testing the environment
"""
import sys
import os
import time
from rlglue import RLGlue
# Initialize RL Glue
RLGlue.RL_init()
RLGlue.RL_env_message('debug=True')
RLGlue.RL_start()
running = True
reward = 0
while running:
result = RLGlue.RL_step()
running = not result.terminal
steps = RLGlue.RL_num_steps()
R = RLGlue.RL_return()
print 'Experiment ended after %d steps with a return of %d' % (steps, R)
RLGlue.RL_cleanup()
def runEpisode(stepLimit):
global whichEpisode
terminal = RLGlue.RL_episode(stepLimit)
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
print "Experiment " + str(args.expid) + "\t Episode " + str(
whichEpisode) + "\t " + str(totalSteps) + " steps \t" + str(
totalReward) + " total reward\t " + str(terminal) + " natural end"
whichEpisode = whichEpisode + 1
#Main Program starts here
# Remember that stepLimit of 0 means there is no limit at all!*/
for t in range(args.numtrials):
print 'trial: ' + str(t)
whichEpisode = 0
taskSpec = RLGlue.RL_init()
steps = np.zeros(args.numeps)
rews = np.zeros(args.numeps)
for ep in range(args.numeps):
runEpisode(args.maxsteps)
steps[ep] = RLGlue.RL_num_steps()
rews[ep] = RLGlue.RL_return()
print 'trial finished, final reward: ' + str(rews[-1])
#with open(args.path+'/'+args.logname+'_'+str(args.expid)+'_'+str(t)+'.pkl','w') as f:
# pickle.dump((steps,rews),f,-1)
RLGlue.RL_cleanup()
print("\n\n----------Stepping through an episode----------")
#We could also start over and do another experiment */
taskSpec = RLGlue.RL_init()
#We could run one step at a time instead of one episode at a time */
#Start the episode */
startResponse = RLGlue.RL_start()
firstObservation = startResponse.o.intArray[0]
firstAction = startResponse.a.intArray[0]
print("First observation and action were: " + str(firstObservation) +
" and: " + str(firstAction))
#Run one step */
stepResponse = RLGlue.RL_step()
#Run until the episode ends*/
while (stepResponse.terminal != 1):
stepResponse = RLGlue.RL_step()
#if (stepResponse.terminal != 1)
#Could optionally print state,action pairs */
#printf("(%d,%d) ",stepResponse.o.intArray[0],stepResponse.a.intArray[0])*/
print("\n\n----------Summary----------")
totalSteps = RLGlue.RL_num_steps()
totalReward = RLGlue.RL_return()
print("It ran for " + str(totalSteps) + " steps, total reward was: " +
str(totalReward))
RLGlue.RL_cleanup()
RLGlue.RL_episode(0)
num_of_steps = num_of_steps + RLGlue.RL_num_steps()
reward = reward + RLGlue.RL_return()
avg_reward_b.append(reward/50)
avg_steps_b.append(num_of_steps/50)
'''
avg_steps_c = []
avg_reward_c = []
RLGlue.RL_env_message("set-start-state 2")
for i in range(100):
num_of_steps = 0
reward = 0
for j in range(50):
RLGlue.RL_episode(0)
num_of_steps = num_of_steps + RLGlue.RL_num_steps()
reward = reward + RLGlue.RL_return()
avg_reward_c.append(reward / 50)
avg_steps_c.append(num_of_steps / 50)
'''
plt.plot(avg_steps_a,'r')
plt.ylabel('Average_Steps')
plt.xlabel('Number of 50 episode runs')
plt.title('Average_steps of A')
plt.show()
plt.plot(avg_reward_a,'r')
plt.ylabel('Average_Reward')
plt.xlabel('Number of 50 episode runs')
plt.title('Average_Reward of A')
