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Vedant Dave 2023-05-29 13:37:22 +02:00
parent 061057fa11
commit 96acac90da
3 changed files with 25 additions and 9 deletions
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2
dynamic_bottleneck.py
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@ -134,7 +134,6 @@ class DynamicBottleneck(object):
x = flatten_two_dims(x) # (None,84,84,4)
if self.aug:
print(x.get_shape().as_list())
x = tf.image.random_crop(x, size=[128*16, 80, 80, 4]) # (None,80,80,4)
x = tf.pad(x, [[0, 0], [4, 4], [4, 4], [0, 0]], "SYMMETRIC") # (None,88,88,4)
x = tf.image.random_crop(x, size=[128*16, 84, 84, 4]) # (None,84,84,4)
@ -158,6 +157,7 @@ class DynamicBottleneck(object):
n_chunks = 8
n = ob.shape[0]
chunk_size = n // n_chunks
assert n % n_chunks == 0
sli = lambda i: slice(i * chunk_size, (i + 1) * chunk_size)

18
rollouts.py
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@ -10,7 +10,7 @@ class Rollout(object):
def __init__(self, ob_space, ac_space, nenvs, nsteps_per_seg, nsegs_per_env, nlumps, envs, policy,
int_rew_coeff, ext_rew_coeff, record_rollouts, dynamic_bottleneck): #, noisy_box, noisy_p):
# int_rew_coeff=1.0, ext_rew_coeff=0.0, record_rollouts=True
self.nenvs = nenvs # 128
self.nenvs = nenvs # 128/64
self.nsteps_per_seg = nsteps_per_seg # 128
self.nsegs_per_env = nsegs_per_env # 1
self.nsteps = self.nsteps_per_seg * self.nsegs_per_env # 128
@ -103,6 +103,22 @@ class Rollout(object):
self.recorder.record(timestep=self.step_count, lump=l, acs=acs, infos=infos, int_rew=self.int_rew[sli],
ext_rew=prevrews, news=news)
import matplotlib.pyplot as plt
from PIL import Image
x = self.buf_obs[sli, t][5][:,:,0]
img = Image.fromarray(x)
img.save('image1.png')
x = self.buf_obs[sli, t][5][:,:,1]
img = Image.fromarray(x)
img.save('image2.png')
x = self.buf_obs[sli, t][5][:,:,2]
img = Image.fromarray(x)
img.save('image3.png')
x = self.buf_obs[sli, t][5][:,:,3]
img = Image.fromarray(x)
img.save('image4.png')
self.step_count += 1
if s == self.nsteps_per_seg - 1: # nsteps_per_seg=128
for l in range(self.nlumps): # nclumps=1

8
run.py
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@ -14,6 +14,7 @@ from baselines.bench import Monitor
from baselines.common.atari_wrappers import NoopResetEnv, FrameStack
from mpi4py import MPI
from dynamic_bottleneck import DynamicBottleneck
from cnn_policy import CnnPolicy
from cppo_agent import PpoOptimizer
@ -26,8 +27,8 @@ import json
getsess = tf.get_default_session
def start_experiment(**args):
make_env = partial(make_env_all_params, add_monitor=True, args=args)
trainer = Trainer(make_env=make_env,
@ -39,7 +40,6 @@ def start_experiment(**args):
print("results will be saved to ", logdir)
trainer.train(saver, logger_dir)
class Trainer(object):
def __init__(self, make_env, hps, num_timesteps, envs_per_process):
self.make_env = make_env
@ -118,10 +118,10 @@ class Trainer(object):
previous_saved_tcount += 1
save_path = saver.save(tf.get_default_session(), os.path.join(logger_dir, "model_"+str(previous_saved_tcount)+".ckpt"))
print("Periodically model saved in path:", save_path)
if self.agent.rollout.stats['tcount'] > self.num_timesteps:
if self.agent.rollout.stats['tcount'] %10000: #self.agent.rollout.stats['tcount'] > self.num_timesteps:
save_path = saver.save(tf.get_default_session(), os.path.join(logger_dir, "model_last.ckpt"))
print("Model saved in path:", save_path)
break
#break
self.agent.stop_interaction()