def main():
#============= Initialize variables ===========#
environment = Environment()
agent = Agent()
# ================= Running episodes =================#
all_rewards = []
batch_size = BATCH_SIZE
for e in range(EPISODES):
state, action, next_state, episode_reward = environment.reset(
) # Reset level in tank
# Running through states in the episode
for t in range(MAX_TIME):
action = agent.act(state)
z = agent.action_choices[action]
terminated, next_state = environment.get_next_state(z, state)
reward = environment.get_reward(next_state, terminated, t)
agent.remember(state, action, next_state, reward, terminated)
episode_reward += reward
if terminated:
break
if environment.show_rendering:
environment.render(z, next_state[-1])
if len(agent.memory) > batch_size:
agent.replay(batch_size)
if keyboard.is_pressed('ctrl+c'):
break
# Live plot rewards
# agent.decay_exploration()
all_rewards.append(episode_reward)
if keyboard.is_pressed('ctrl+c'):
break
if LIVE_REWARD_PLOT:
environment.plot(all_rewards, agent.epsilon)
if not environment.running:
break
print("##### {} EPISODES DONE #####".format(e))
print("Max rewards for all episodes: {}".format(np.max(all_rewards)))
print("Mean rewards for the last 10 episodes: {}".format(
np.mean(all_rewards[-10:])))
if SAVE_ANN_MODEL:
print("ANN_Model was saved")
def main():
# ============= Initialize variables and objects ===========#
max_mean_reward = 50 * len(TANK_PARAMS)
environment = Environment(TANK_PARAMS, TANK_DIST, MAIN_PARAMS)
agent = Agent(AGENT_PARAMS)
mean_episode = MAIN_PARAMS["MEAN_EPISODE"]
episodes = MAIN_PARAMS["EPISODES"]
all_rewards = []
all_mean_rewards = []
# ================= Running episodes =================#
try:
for e in range(episodes):
states, episode_reward = environment.reset() # Reset level in tank
for t in range(MAIN_PARAMS["MAX_TIME"]):
actions = agent.act(states[-1]) # get action choice from state
z = agent.get_z(actions)
terminated, next_state = environment.get_next_state(
z, states[-1], t) # Calculate next state with action
rewards = sum_rewards(
next_state, terminated,
get_reward) # get reward from transition to next state
# Store data
episode_reward.append(np.sum(rewards))
states.append(next_state)
agent.remember(states, rewards, terminated, t)
if environment.show_rendering:
environment.render(z)
if True in terminated:
break
all_rewards.append(np.sum(np.array(episode_reward)))
# Print mean reward and save better models
if e % mean_episode == 0 and e != 0:
mean_reward = np.mean(all_rewards[-mean_episode:])
all_mean_rewards.append(mean_reward)
print("{} of {}/{} episodes\
reward: {} exp_1: {} exp_2: {}".format(
mean_episode,
e,
episodes,
round(mean_reward, 2),
round(agent.epsilon[0], 2),
round(agent.epsilon[1], 2),
))
if agent.save_model_bool:
max_mean_reward = agent.save_model(mean_reward,
max_mean_reward)
# Train model
if agent.is_ready():
agent.Qreplay(e)
if keyboard.is_pressed("ctrl+x"):
break
if environment.live_plot:
environment.plot(all_rewards, agent.epsilon)
if not environment.running:
break
# if agent.epsilon <= agent.epsilon_min:
# break
except KeyboardInterrupt:
pass
print("Memory length: {}".format(len(agent.memory)))
print("##### {} EPISODES DONE #####".format(e + 1))
print("Max rewards for all episodes: {}".format(np.max(all_rewards)))
plt.ioff()
plt.clf()
x_range = np.arange(0, e - e % mean_episode, mean_episode)
plt.plot(x_range, all_mean_rewards)
plt.ylabel("Mean rewards of last {} episodes".format(mean_episode))
plt.show()