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In this chapter we’ve talked about ALF configuration and pipeline based on the minimal example. We’ve shown that ALF is essentially a pipeline that connects different components which can be customized by a conf file. Moreover, ALF provides various arms for doing RL research.
Below are a series of examples for writing training files using ALF, from simple to advanced usage. Each chapter is a detailed, step-by-step guide walking through key ALF concepts. All the tutorial code files can be found under <ALF_ROOT>/alf/examples/tutorial.
Agent Learning Framework (ALF) is a reinforcement learning framework emphasizing on the flexibility and easiness of implementing complex algorithms involving many different components. ALF is built on PyTorch. The development of previous version based on Tensorflow 2.1 has stopped as of Feb 2020.
AlgorithmTypeReferenceOn-policy RLOn-policy RLSchulman et al. "Proximal Policy ...On-policy RLCobbe et al. "Phasic Policy Gradient" ...Off-policy RLHasselt et al. "Deep Reinforcement ...alf. config ('cool_func', cool_arg1 = 'new_value', cool_arg2 = 'another_value') alf. config ('Worker.func', b = 3) alf. config ('func', b = 3) # 'Worker.func' can be uniquely identified by 'func' alf. config ({'dumb_func.b': 3, 'Worker.job1': 2 # now the default value of job1 for Worker() becomes 2.
Jul 22, 2024 · A Python package building on top of ALF, meant to simplify the different steps of working with ALF, including: Obtaining and compiling the ALF source code. Preparing and running simulations. Postprocessing and displaying the data obtained during the simulation. It introduces:
The documentation for ALF’s Python interface, pyALF, is available here. You can see a presentation of its more advanced features (as of Feb. 2022) here. For those interested in programming with ALF, also available is a documentation automatically generated from its Fortran 2008 code: Doxigen.
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There we showed very basic usage of the alf.bin.play module by loading a trained model and rendering the environment to the screen or a video. Now we describe how to utilize ALF’s advanced play features to evaluate or debug a model in depth.
