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deeprl-baselines's Introduction

Baselines

Our code is based on OpenAI Baselines, which is a set of high-quality implementations of reinforcement learning algorithms. Our code is aimed to provide more algorithms which is not included by OpenAI baselines, such as C51 and rainbow, as well as improvements.

These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.

You can install it by typing:

git clone https://github.com/cxxgtxy/deeprl-baselines.git
cd deeprl-baselines
pip install -e .

deeprl-baselines's People

Contributors

aaorris avatar cxxgtxy avatar definitelyuncertain avatar farbeiza avatar jonasschneider avatar joschu avatar kuangshengkai avatar linzichuan avatar louiehelm avatar mansimov avatar matthiasplappert avatar mirceamironenco avatar mkarutz avatar ngc92 avatar omoindrot avatar ppwwyyxx avatar quanvuong avatar ryanjulian avatar siemanko avatar stevenschmatz avatar tiagosgc avatar unixpickle avatar welinder avatar yenchenlin avatar zach-nervana avatar

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deeprl-baselines's Issues

Shape before tf.add(atoms_scores, atoms_action_adv) in distributional_dueling_model

Hi cxxgtxy,

I print some shapes in distributional_dueling_model, I wonder the outcome of tf.add(atoms_scores, atoms_action_adv), is it necessary to reshape atoms_scores from (?, 1, 51) to (?, 6, 51) before tf.add()?

img_in: (?, 84, 84, 4)
out: (?, 11, 11, 64)
conv_flatten: (?, 7744)
state_hidden: (?, 512)
state_score: (?, 51)
atoms_scores: (?, 1, 51)

actions_hidden: (?, 512)
action_scores: (?, 306)
atoms_action_adv: (?, 6, 51)
atoms_action_adv_mean: (?, 51)
atoms_action_adv - tf.expand_dims(atoms_action_adv_mean, 1): (?, 6, 51)

tf.add(atoms_scores, atoms_action_adv): (?, 6, 51)

Thank you for your help.

Is there any NoisyNet implementation?

Hi cxxgtxy,

Is there any NoisyNet implementation in the Rainbow baseline?
Doy you have any recommanded implementation of efficient NoisyNet?
Thank you for your help.

ImportError: No module named 'azure.common'

Traceback (most recent call last):
File "C:\Program Files\Python35\lib\runpy.py", line 184, in _run_module_as_main
"main", mod_spec)
File "C:\Program Files\Python35\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Program Files\Python35\lib\site-packages\baselines-0.1.4-py3.5.egg\baselines\rainbow\experiments\atari\rainbow.py", line 27, in
File "C:\Program Files\Python35\lib\site-packages\baselines-0.1.4-py3.5.egg\baselines\common\azure_utils.py", line 5, in
ImportError: No module named 'azure.common'

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