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Update OpenAI Lander example #252

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Jul 28, 2023
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Update OpenAI Lander example #252

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20 changes: 11 additions & 9 deletions examples/openai-lander/evolve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
import neat
import visualize

NUM_CORES = 8
NUM_CORES = multiprocessing.cpu_count()

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

Expand Down Expand Up @@ -86,21 +86,22 @@ def __init__(self, num_workers):
def simulate(self, nets):
scores = []
for genome, net in nets:
observation = env.reset()
observation_init_vals, 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)
output = net.activate(observation_init_vals)
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Isn't this wrong? Shouldn't you have named this observation? Now it just feeds the initial observation every time through the loop, and the observation never changes. Same issue below!

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@markste-in markste-in Aug 13, 2023 •
edited
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i think you are right. I created another PR ... maybe have a look at it and feel free to comment if u find something
#274

action = np.argmax(output)

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

if done:
if terminated:
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See comment for line 223 -> same topic

break

data = np.array(data)
Expand Down Expand Up @@ -202,7 +203,7 @@ def run():
solved = True
best_scores = []
for k in range(100):
observation = env.reset()
observation_init_vals, observation_init_info = env.reset()
score = 0
step = 0
while 1:
Expand All @@ -211,14 +212,15 @@ def run():
# determine the best action given the current state.
votes = np.zeros((4,))
for n in best_networks:
output = n.activate(observation)
output = n.activate(observation_init_vals)
votes[np.argmax(output)] += 1

best_action = np.argmax(votes)
observation, reward, done, info = env.step(best_action)
# Note: done has been deprecated.
observation, reward, terminated, done, info = env.step(best_action)
score += reward
env.render()
if done:
if terminated:
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I would use the truncated state since it seems more to behave like the old "done".
According to the docstring truncated means:

truncated (bool): whether a truncation condition outside the scope of the MDP is satisfied.
               Typically a timelimit, but could also be used to indicate agent physically going out of bounds.
               Can be used to end the episode prematurely before a `terminal state` is reached.

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It looks like the correct action here would have been if terminated or truncated.

break

ec.episode_score.append(score)
Expand Down