def main_DQN_plus_greedy():
GREEDY_TOTAL_NUM_EPISODES = 1000
GREEDY_NUM_EPISODES = GREEDY_TOTAL_NUM_EPISODES // 3
env = AgarioEnv(render=RENDER,
speed_scale=SPEED_SCALE,
display_text=DISPLAY_TEXT,
grid_resolution=GRID_RESOLUTION)
agent = DQNAgent(height=GRID_RESOLUTION,
width=GRID_RESOLUTION,
input_channels=2,
num_actions=ACTION_DISCRETIZATION,
loadpath='')
greedy = Greedy()
env.seed(41)
agent.seed(41)
for episode in range(GREEDY_TOTAL_NUM_EPISODES):
state = env.reset()
done = False
new_state = None
raw_action, action = None, None
reward = 0
num_steps = 0
is_greedy_episode = episode < GREEDY_NUM_EPISODES
while not done:
if is_greedy_episode:
action = greedy.get_action(state)
raw_action = agent.angle_to_action(action)
# print(f'angle: {action}, raw_action: {raw_action}')
else:
raw_action = agent.get_action(state)
action = agent.action_to_angle(raw_action)
for _ in range(NUM_SKIP_FRAMES):
if RENDER:
env.render()
new_state, reward, done, _ = env.step(action)
num_steps += 1
# print(f'step = {num_steps}')
if done or num_steps > MAX_STEPS:
new_state = None
done = True
agent.memory.push(state, raw_action, new_state, reward)
agent.optimize()
if done:
print(
f'{"Greedy" if is_greedy_episode else "DQN" } episode done, max_mass: {state.mass}'
)
if not is_greedy_episode:
agent.max_masses.append(state.mass)
if num_steps % agent.TARGET_UPDATE == 0:
# print(f'UPDATING TARGET')
agent.target_net.load_state_dict(agent.policy_net.state_dict())
state = new_state
print(f'Complete')
torch.save(
agent.policy_net.state_dict(),
f'model_GREEDY_DQN_{NUM_EPISODES}_{str(datetime.now()).replace(" ", "_")}_episodes.model'
)
agent.print_final_stats()
env.close()
def main_DQN():
env = AgarioEnv(render=RENDER,
speed_scale=SPEED_SCALE,
display_text=DISPLAY_TEXT,
grid_resolution=GRID_RESOLUTION)
agent = DQNAgent(height=GRID_RESOLUTION,
width=GRID_RESOLUTION,
input_channels=2,
num_actions=ACTION_DISCRETIZATION,
loadpath='')
# env.seed(41)
# agent.seed(41)
for episode in range(NUM_EPISODES):
state = env.reset()
done = False
new_state = None
reward = 0
num_steps = 0
while not done:
raw_action = agent.get_action(state)
action = agent.action_to_angle(raw_action)
for _ in range(NUM_SKIP_FRAMES):
if RENDER:
env.render()
new_state, reward, done, _ = env.step(action)
num_steps += 1
# print(f'step = {num_steps}')
if done or num_steps > MAX_STEPS:
new_state = None
done = True
agent.memory.push(state, raw_action, new_state, reward)
agent.optimize()
if done:
print(f'Episode {episode} done, max_mass = {state.mass}')
agent.max_masses.append(state.mass)
agent.print_final_stats()
if num_steps % agent.TARGET_UPDATE == 0:
# print(f'UPDATING TARGET')
agent.target_net.load_state_dict(agent.policy_net.state_dict())
state = new_state
if episode % WEIGHTS_SAVE_EPISODE_STEP == 0:
torch.save(
agent.policy_net.state_dict(),
f'DQN_weights/model_{episode}_{str(datetime.now()).replace(" ", "_")}_episodes.model'
)
np.savetxt(
f'DQN_weights/model_{episode}_{str(datetime.now()).replace(" ", "_")}_episodes.performance',
np.array(agent.max_masses))
print(f'Complete')
torch.save(
agent.policy_net.state_dict(),
f'model_{NUM_EPISODES}_{str(datetime.now()).replace(" ", "_")}_episodes.model'
)
np.savetxt(
f'DQN_weights/model_{NUM_EPISODES}_{str(datetime.now()).replace(" ", "_")}_episodes.performance',
np.array(agent.max_masses))
agent.print_final_stats()
env.close()
