def main(args):
# load env
env = gym.make('CartPole-v0')
# load agent
agent = DQN(env)
agent.construct_model(args.gpu)
# load model or init a new
saver = tf.train.Saver()
if args.model_path is not None:
# reuse saved model
saver.restore(agent.sess, args.model_path)
else:
# build a new model
agent.init_var()
# training loop
for ep in range(args.ep):
# reset env
total_rewards = 0
state = env.reset()
while True:
env.render()
# sample actions
action = agent.sample_action(state, policy='greedy')
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
# state shift
state = next_state
if done:
break
print('Ep%s Reward: %s ' % (ep + 1, total_rewards))
def main(args):
# load env
env = gym.make('CartPole-v0')
# load agent
agent = DQN(env)
agent.construct_model(args.gpu)
# load model or init a new
saver = tf.train.Saver()
if args.model_path is not None:
# reuse saved model
saver.restore(agent.sess, args.model_path)
else:
# build a new model
agent.init_var()
# training loop
for ep in range(args.ep):
# reset env
total_rewards = 0
state = env.reset()
while True:
env.render()
# sample actions
action = agent.sample_action(state, policy='greedy')
# act!
next_state, reward, done, _ = env.step(action)
total_rewards += reward
# state shift
state = next_state
if done:
break
print('Ep%s Reward: %s ' % (ep+1, total_rewards))
def main(args):
set_random_seed(args.seed)
env = gym.make("CartPole-v0")
agent = DQN(env, args)
agent.construct_model(args.gpu)
# load pre-trained models or init new a model.
saver = tf.train.Saver(max_to_keep=1)
if args.model_path is not None:
saver.restore(agent.sess, args.model_path)
ep_base = int(args.model_path.split('_')[-1])
best_mean_rewards = float(args.model_path.split('/')[-1].split('_')[0])
else:
agent.sess.run(tf.global_variables_initializer())
ep_base = 0
best_mean_rewards = None
rewards_history, steps_history = [], []
train_steps = 0
# Training
for ep in range(args.max_ep):
state = env.reset()
ep_rewards = 0
for step in range(env.spec.max_episode_steps):
# pick action
action = agent.sample_action(state, policy='egreedy')
# execution action.
next_state, reward, done, debug = env.step(action)
train_steps += 1
ep_rewards += reward
# modified reward to speed up learning
reward = 0.1 if not done else -1
# learn and Update net parameters
agent.learn(state, action, reward, next_state, done)
state = next_state
if done:
break
steps_history.append(train_steps)
if not rewards_history:
rewards_history.append(ep_rewards)
else:
rewards_history.append(rewards_history[-1] * 0.9 +
ep_rewards * 0.1)
# decay epsilon
if agent.epsilon > args.final_epsilon:
agent.epsilon -= (args.init_epsilon -
args.final_epsilon) / args.max_ep
# evaluate during training
if ep % args.log_every == args.log_every - 1:
total_reward = 0
for i in range(args.test_ep):
state = env.reset()
for j in range(env.spec.max_episode_steps):
action = agent.sample_action(state, policy='greedy')
state, reward, done, _ = env.step(action)
total_reward += reward
if done:
break
current_mean_rewards = total_reward / args.test_ep
print('Episode: %d Average Reward: %.2f' %
(ep + 1, current_mean_rewards))
# save model if current model outperform the old one
if best_mean_rewards is None or (current_mean_rewards >=
best_mean_rewards):
best_mean_rewards = current_mean_rewards
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
save_name = args.save_path + str(round(best_mean_rewards, 2)) \
+ '_' + str(ep_base + ep + 1)
saver.save(agent.sess, save_name)
print('Model saved %s' % save_name)
plt.plot(steps_history, rewards_history)
plt.xlabel('steps')
plt.ylabel('running avg rewards')
plt.show()
def main(args):
set_random_seed(args.seed)
env = gym.make('CartPole-v0')
agent = DQN(env, args)
agent.construct_model(args.gpu)
# load pretrained models or init new a model.
saver = tf.train.Saver(max_to_keep=1)
if args.model_path is not None:
saver.restore(agent.sess, args.model_path)
ep_base = int(args.model_path.split('_')[-1])
best_mean_rewards = float(args.model_path.split('/')[-1].split('_')[0])
else:
agent.sess.run(tf.global_variables_initializer())
ep_base = 0
best_mean_rewards = None
rewards_history, steps_history = [], []
train_steps = 0
# Training
for ep in range(args.max_ep):
state = env.reset()
ep_rewards = 0
for step in range(env.spec.timestep_limit):
# pick action
action = agent.sample_action(state, policy='egreedy')
# Execution action.
next_state, reward, done, debug = env.step(action)
train_steps += 1
ep_rewards += reward
# modified reward to speed up learning
reward = 0.1 if not done else -1
# Learn and Update net parameters
agent.learn(state, action, reward, next_state, done)
state = next_state
if done:
break
steps_history.append(train_steps)
if not rewards_history:
rewards_history.append(ep_rewards)
else:
rewards_history.append(
rewards_history[-1] * 0.9 + ep_rewards * 0.1)
# Decay epsilon
if agent.epsilon > args.final_epsilon:
agent.epsilon -= (args.init_epsilon - args.final_epsilon) / args.max_ep
# Evaluate during training
if ep % args.log_every == args.log_every-1:
total_reward = 0
for i in range(args.test_ep):
state = env.reset()
for j in range(env.spec.timestep_limit):
action = agent.sample_action(state, policy='greedy')
state, reward, done, _ = env.step(action)
total_reward += reward
if done:
break
current_mean_rewards = total_reward / args.test_ep
print('Episode: %d Average Reward: %.2f' %
(ep + 1, current_mean_rewards))
# save model if current model outpeform the old one
if best_mean_rewards is None or (current_mean_rewards >= best_mean_rewards):
best_mean_rewards = current_mean_rewards
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
save_name = args.save_path + str(round(best_mean_rewards, 2)) \
+ '_' + str(ep_base + ep + 1)
saver.save(agent.sess, save_name)
print('Model saved %s' % save_name)
# plot training rewards
plt.plot(steps_history, rewards_history)
plt.xlabel('steps')
plt.ylabel('running avg rewards')
plt.show()
def main(args):
# Hyper parameters
MAX_EPISODE = 10000 # training episode
INITIAL_EPSILON = 0.5 # starting value of epsilon
FINAL_EPSILON = 0.01 # final value of epsilon
TEST_EPISODE = 100
env = gym.make('CartPole-v0')
agent = DQN(env, double_q=args.double)
agent.construct_model(args.gpu)
saver = tf.train.Saver(max_to_keep=2)
if args.model_path is not None:
saver.restore(agent.sess, args.model_path)
ep_base = int(args.model_path.split('_')[-1])
mean_rewards = float(args.model_path.split('/')[-1].split('_')[0])
else:
agent.sess.run(tf.global_variables_initializer())
ep_base = 0
mean_rewards = None
# Training
for ep in range(MAX_EPISODE):
state = env.reset()
for step in range(env.spec.timestep_limit):
# pick action
action = agent.sample_action(state, policy='egreedy')
# Execution action.
next_state, reward, done, debug = env.step(action)
# modified reward to speed up learning
reward = 0.1 if not done else -1
# Learn and Update net parameters
agent.learn(state, action, reward, next_state, done)
state = next_state
if done:
break
# Update epsilon
if agent.epsilon > FINAL_EPSILON:
agent.epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / MAX_EPISODE
# Evaluate during training
if ep % args.log_every == args.log_every - 1:
total_reward = 0
for i in range(TEST_EPISODE):
state = env.reset()
for j in range(env.spec.timestep_limit):
action = agent.sample_action(state, policy='greedy')
state, reward, done, _ = env.step(action)
total_reward += reward
if done:
break
mean_rewards = total_reward / float(TEST_EPISODE)
print('Episode:', ep + 1, ' Average Reward:', mean_rewards)
print('Global steps:', agent.global_step)
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
save_name = args.save_path + str(round(mean_rewards, 2)) + '_' \
+ str(ep_base+ep+1)
saver.save(agent.sess, save_name)