n_games = 200
best_score = env.reward_range[0]
score_history = []
load_checkpoints = False
if load_checkpoints:
agent.load_models()
env.render(mode='human')
for i in range(n_games):
observation = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.remember(observation, action, reward, observation_, done)
#if not load_checkpoints:
agent.learn()
observation = observation_
score_history.append(score)
avg_score = np.mean(score_history[-100:])
# if avg_score > best_score:
# best_score = avg_score
# #if not load_checkpoints:
# agent.save_models()
print('episode ', i, ' score %.1f ' % score,
'avg score %.1f ' % avg_score)
def main():
Hyper.init()
env = make_env(
Constants.env_id) # See wrapper code for environment in atari_image.py
Hyper.n_actions = env.action_space.n
shape = (env.observation_space.shape)
agent = Agent(input_dims=shape, env=env, n_actions=env.action_space.n)
filename = f"{Constants.env_id}_games{Hyper.n_games}_alpha{Hyper.alpha}.png"
figure_file = f'plots/{filename}'
best_ave_score = env.reward_range[0]
best_score = 0
score_history = []
load_checkpoint = False
if load_checkpoint:
agent.load_models()
env.render(mode='human')
total_steps = 0
game_id = 0
for i in range(Hyper.n_games):
game_id += 1
if game_id % 20 == 0:
Hyper.alpha = Hyper.alpha * 1.2
Hyper.beta = Hyper.beta * 1.2
observation = env.reset()
done = False
steps = 0
score = 0
while not done:
# Sample action from the policy
action = agent.choose_action(observation)
# Sample transition from the environment
new_observation, reward, done, info = env.step(action)
steps += 1
total_steps += 1
# Store transition in the replay buffer
agent.remember(observation, action, reward, new_observation, done)
if not load_checkpoint:
agent.learn()
score += reward
observation = new_observation
score_history.append(score)
avg_score = np.mean(score_history[-100:])
if score > best_score:
best_score = score
if avg_score > best_ave_score:
best_ave_score = avg_score
if not load_checkpoint:
agent.save_models()
episode = i + 1
print(
f"episode {episode}: score {score}, best_score {best_score}, best ave score {best_ave_score}, trailing 100 games avg {avg_score}, steps {steps}, total steps {total_steps}"
)
print(f"total number of steps taken: {total_steps}")
if not load_checkpoint:
x = [i + 1 for i in range(Hyper.n_games)]
plot_learning_curve(x, score_history, figure_file)