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donkey_gym_rnn.py
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donkey_gym_rnn.py
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import gym, gym_donkeycar, donkeycar_modified
from agent import DQNAgent
import tensorflow as tf
import cv2, random, time
import numpy as np
from csv import writer;
from utils import *;
def calculateThrottle(velocity, max_velocity, max_acceleration):
return min((max_velocity - velocity)/12.5 + 0.05, max_acceleration) * (velocity < max_velocity)
def main():
# enable GPU memory growth
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# model & training information
model_name = input("Model name -> ");
load_trained = input("Load trained (y/n)? ").lower() == "y";
epsilon = float(input("Epsilon -> "));
episode_count = int(input("Episode count -> "));
model_location = "models/" + model_name + "/";
model_path = model_location + ("model_trained.h5" if load_trained else "model.h5");
print("Loading", model_path, "with epsilon", epsilon);
agent = DQNAgent(model_path, epsilon);
try: agent.memory = json.load(model_location + "data.json");
except: agent.memory = [];
# training information
resizeScale = (40, 30);
batch_size = 12;
frame_n = 3;
max_cte = 4.35;
# max_cte = 3.5;
# statistics
score = [];
rewards = [];
highest_score = 0;
highest_reward = 0;
max_score = None;
# velocity
max_velocity = 10.0
max_acceleration = 0.75
# steering
max_steering = 0.75
steering_step = 2*max_steering/(agent.action_space-1)
steering_table = [i*steering_step-max_steering for i in range(agent.action_space)]
file = open("log.csv", "w+", newline="");
log = writer(file);
log.writerow(['Episode','Timestep', 'Avg Steer', 'Min Reward', 'Avg Reward', 'Max Reward', 'Episode Length', 'Reward Sum', 'Max Q steer', 'Max Q throttle', 'Epsilon','Episode Time', 'Avg Speed','Max Speed','Min CTE','Avg CTE','Max CTE','Distance', "Average Throttle", "Max Throttle", "Min Throttle", "Average Absolute CTE", "Min Absolute CTE", "Max Absolute CTE"]);
# setup donkey environment
conf = {
# "exe_path":"remote",
"exe_path":"D:/sdsandbox/build2/donkey_sim.exe",
"host":"127.0.0.1",
"port":9091,
"body_style":"donkey",
"body_rgb":(128, 128, 128),
"car_name":"rl",
"font_size":100
}
# env = gym.make("donkey-generated-roads-v0", conf=conf)
env = gym.make("donkey-generated-track-v0", conf=conf)
env.viewer.handler.max_cte = max_cte;
cv2.namedWindow("camera");
first_train = True;
first_start = time.time();
timestep = 0;
success_episodes = 0;
max_laps = 5;
for e in range(episode_count):
# at each episode, reset the environment
state = env.reset();
states = np.empty((frame_n, resizeScale[1], resizeScale[0], 3));
states[0] = preprocessImage(state, resizeScale);
need_frames = frame_n-1;
done = False;
score.append(0);
rewards.append(0.0);
last_velocity = [0.0];
laps = 0;
start = time.time();
# logging
steers = [];
throttles = [];
rewards_ = [];
velocities = [];
ctes = [];
ctes_absolute = [];
max_q_steer = 0.0;
distance = 0.0;
distance_time = start;
while not done and (score[-1] < max_score if max_score else True):
if need_frames > 0:
next_state, reward, done, info = env.step([steering_table[random.randint(0, agent.action_space-1)], 0.15]);
states[frame_n-need_frames] = preprocessImage(next_state, resizeScale);
need_frames -= 1
last_velocity.append(info["speed"]);
continue
# select action, observe environment, calculate reward
action, Q = agent.act(np.asarray([states]));
steering = steering_table[action];
throttle = calculateThrottle(last_velocity[-1], max_velocity, max_acceleration);
next_state, reward, done, info = env.step([steering, throttle]);
last_velocity.append(round(info["speed"], 4));
img = cv2.resize(next_state, (320, 240), interpolation=cv2.INTER_AREA)
cv2.imshow("camera", img)
reward = 0.0 if not done else -1.0;
if abs(info["cte"]) >= max_cte:
done = True
reward = -1.0
if not done:
reward = (1.0 - (abs(info["cte"]) / max_cte));
# for track
if info["lap_finished"]:
laps += 1;
if laps == max_laps:
done = True
timestep += 1;
score[-1] += 1;
rewards[-1] += reward;
next_states = np.roll(states, -1, axis=0);
next_states[-1] = preprocessImage(next_state, resizeScale);
# save experience and update current state
agent.remember([states], action, reward, [next_states], done);
states = next_states;
if not first_train:
agent.replay(batch_size);
# logging
steers.append(steering);
throttles.append(throttle);
rewards_.append(reward);
velocities.append(last_velocity[-1]);
ctes.append(info["cte"]);
ctes_absolute.append(abs(info["cte"]));
distance += last_velocity[-1]*(time.time()-distance_time)
distance_time = time.time()
if Q != None and (max_q_steer == None or Q > max_q_steer):
max_q_steer = Q;
cv2.waitKey(1);
# for roads
# if distance > 1900:
# laps = max_laps
# logging
if score[-1] > 0:
log.writerow([e, timestep, round(mean(steers), 2), round(min(rewards_), 2), round(mean(rewards_), 2), round(max(rewards_), 2), score[-1], round(rewards[-1], 2), round(max_q_steer, 2), 0, agent.epsilon, round(time.time()-start,2), round(mean(velocities), 2), round(max(velocities), 2), round(min(ctes), 2), round(mean(ctes), 2), round(max(ctes), 2), round(distance, 2), round(mean(throttles), 2), round(max(throttles), 2), round(min(throttles), 2), round(mean(ctes_absolute), 2), round(min(ctes_absolute), 2), round(max(ctes_absolute), 2)]);
else: # sometimes, something goes really wrong... don't count this episode
e -= 1
file.flush();
# fix for persisting throttle bug
env.step([0.0, -0.03]);
if len(agent.memory) > batch_size*4 and first_train:
agent.replay(batch_size);
agent.act(np.asarray([states]));
first_train = False;
if len(score) > 20: score = score[-20:];
if len(rewards) > 20: rewards = rewards[-20:];
if score[-1] >= highest_score:
highest_score = score[-1];
if rewards[-1] >= highest_reward:
highest_reward = rewards[-1]
agent.save();
print("episode: {}/{}, steps: {}, reward: {}, highest reward: {}, average: {}, laps: {}, e: {:.2}, memory: {}, replays: {}"
.format(e+1, episode_count, score[-1], round(rewards[-1], 2), round(highest_reward, 2), round(mean(rewards), 2), laps, agent.epsilon, len(agent.memory), agent.replays));
if (e+1) % 5 == 0:
print("Took", round((time.time()-start)/60, 2), "minutes\n");
start = time.time();
agent.merge_models();
if laps == max_laps:
success_episodes += 1;
else:
success_episodes = 0;
if success_episodes == 5:
print("Training successfull! Time: {} minutes.".format(round((time.time()-first_start)/60.0, 2)));
agent.save("end.h5");
file.close();
break;
agent.save();
print("Total training time:", round((time.time()-first_start)/60, 2), "minutes");
if __name__ == "__main__":
main()