experts_chkpt_lis = [
'./core/checkpoints/q_learning/skip_connection/q5_train_atari_nature/deepdqn_weights/', './core/checkpoints/q_learning/skip_connection/q5_train_atari_nature/resnet_weights/', './core/checkpoints/policy_gradients/policy_network.ckpt']
experts = []
#temp_sess = None
for meta_path, chkpt_path in zip(experts_meta_lis, experts_chkpt_lis):
print([n.name for n in tf.get_default_graph().as_graph_def().node])
if "deepdqn" in meta_path:
model = NatureQN(env, config)
if "resnet" in meta_path:
model = ResnetQN(env, config)
if "policy" in meta_path:
continue
# if temp_sess == None:
#temp_sess = model.sess
model.initialize(meta_path, chkpt_path)
experts.append(model)
# with model.graph.as_default():
print("LOADED ALL MODELS")
for i in range(len(experts)):
guide = experts[i]
guide_experience = [[]]
num_points = 0
state = env.reset()
guide_replay_buffer = ReplayBuffer(
config.buffer_size, config.state_history)
while True:
# store last state in buffer
idx = guide_replay_buffer.store_frame(state)
If so, please report your hyperparameters.
You'll find the results, log and video recordings of your agent every 250k under
the corresponding file in the results folder. A good way to monitor the progress
of the training is to use Tensorboard. The starter code writes summaries of different
variables.
To launch tensorboard, open a Terminal window and run
tensorboard --logdir=results/
Then, connect remotely to
address-ip-of-the-server:6006
6006 is the default port used by tensorboard.
"""
if __name__ == '__main__':
# make env
env = gym.make(config.env_name)
env = MaxAndSkipEnv(env, skip=config.skip_frame)
env = PreproWrapper(env,
prepro=greyscale,
shape=(80, 80, 1),
overwrite_render=config.overwrite_render)
# load model
model = NatureQN(env, config)
model.initialize()
loaded = load_model(model)
assert loaded != False, "Loading failed"
# evaluate one episode of data
model.evaluate(env, 1)
