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fix: used initial observation the entire time #274

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fix: used initial observation the entire time #274

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5bea745
fix: wrong observation
Aug 13, 2023
ec780d7
fix: done -> truncated
Aug 13, 2023
730d400
fix: make sure best_genomes exist and make one print statement more d…
Aug 13, 2023
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43 changes: 23 additions & 20 deletions examples/openai-lander/evolve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,9 +20,8 @@

env = gym.make('LunarLander-v2')

print("action space: {0!r}".format(env.action_space))
print("observation space: {0!r}".format(env.observation_space))

# print("action space: {0!r}".format(env.action_space))
# print("observation space: {0!r}".format(env.observation_space))

class LanderGenome(neat.DefaultGenome):
def __init__(self, key):
Expand Down Expand Up @@ -86,22 +85,21 @@ def __init__(self, num_workers):
def simulate(self, nets):
scores = []
for genome, net in nets:
observation_init_vals, observation_init_info = env.reset()
observation, observation_init_info = env.reset()
step = 0
data = []
while 1:
step += 1
if step < 200 and random.random() < 0.2:
action = env.action_space.sample()
else:
output = net.activate(observation_init_vals)
output = net.activate(observation)
action = np.argmax(output)

# Note: done has been deprecated.
observation, reward, terminated, done, info = env.step(action)
observation, reward, terminated, truncated, info = env.step(action)
data.append(np.hstack((observation, action, reward)))

if terminated:
if terminated or truncated:
break

data = np.array(data)
Expand Down Expand Up @@ -169,7 +167,7 @@ def run():
pop.add_reporter(neat.StdOutReporter(True))
# Checkpoint every 25 generations or 900 seconds.
pop.add_reporter(neat.Checkpointer(25, 900))

best_genomes = None
# Run until the winner from a generation is able to solve the environment
# or the user interrupts the process.
ec = PooledErrorCompute(NUM_CORES)
Expand Down Expand Up @@ -203,7 +201,7 @@ def run():
solved = True
best_scores = []
for k in range(100):
observation_init_vals, observation_init_info = env.reset()
observation, observation_init_info = env.reset()
score = 0
step = 0
while 1:
Expand All @@ -212,31 +210,40 @@ def run():
# determine the best action given the current state.
votes = np.zeros((4,))
for n in best_networks:
output = n.activate(observation_init_vals)
output = n.activate(observation)
votes[np.argmax(output)] += 1

best_action = np.argmax(votes)
# Note: done has been deprecated.
observation, reward, terminated, done, info = env.step(best_action)

observation, reward, terminated, truncated, info = env.step(best_action)
score += reward
env.render()
if terminated:
if terminated or truncated:
break

ec.episode_score.append(score)
ec.episode_length.append(step)

best_scores.append(score)
avg_score = sum(best_scores) / len(best_scores)
print(k, score, avg_score)
print(f'Solved {k} times. '
f'Last score {score}, '
f'Average score {avg_score}, '
f'Max score {np.max(best_scores)}')
if avg_score < 200:
solved = False
break

if solved:
print("Solved.")
break
except KeyboardInterrupt:
print("User break.")
break

# Save the winners.
finally:
# Save the winners.
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You could put an if best_genomes: here to handle the case where the user terminates within the first 5 generations (if I read it correctly, best_genomes will be undefined in that case).

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good point. I defined best_genomes = None in the top to make sure the variable exists and can be checked and added an if best_genomes on the bottom

if best_genomes:
for n, g in enumerate(best_genomes):
name = 'winner-{0}'.format(n)
with open(name + '.pickle', 'wb') as f:
Expand All @@ -245,10 +252,6 @@ def run():
visualize.draw_net(config, g, view=False, filename=name + "-net.gv")
visualize.draw_net(config, g, view=False, filename=name + "-net-pruned.gv", prune_unused=True)

break
except KeyboardInterrupt:
print("User break.")
break

env.close()

Expand Down