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MLDLRL


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Reinforcement Learning for Machine Learning and Deep Learning course
PoliTo 2021/2022

In the following, the whole project is briefly presented in how it works and how the various scripts should be executed to reproduce our results. We worked with some basic RL algorithms to make our friendly hopper able to jump to infinity and beyond.

Table of Contents
  1. About The Project
  2. Getting Started
  3. Uniform Domain Randomization
  4. Adaptive Domain Randomization
  5. Tests
  6. Variant
  7. Contacts

About The Project

As per the guidelines, this project implements some basic RL Algorithms to learn an efficient policy that makes the HOPPER able to jump forward, along the x axis, maximazing its horizontal velocity. The way the code is organized is very simple: a preliminary inspection of the main algorithms employed is done via the interface.py file, which makes use of the content of the following folders: agent/ commons/ env/ /models and /policies. Step3 and Step4 of the task are implemented in domain_randomization/ and adaptive_dr/ respectively, in a way that their content can be executed without relying on other files. In the following more informations will be provided.

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Getting Started

The algorithms we implemented are REINFORCE (with three different reward-systems implemented, i.e. baseline, reward to-go, and standard REINFORCE), ActorCritic, PPO and TRPO. REINFORCE and ActorCritic are implemented from scratch, and their content can be found in agents/ and policies/; since we tried to keep the structure simple and intuitive, in agents/ there are the main classes that perform the update operation of a given policy, while in policies/ there are two classes: fraNET.py and tibNET.py. For the purpose of solving the task as indicated in the guidelines, tibNET is the only policy approximation network employed in the whole project, while fraNET served just for debugging purposes, therefore its content shall not evaluated. One can interact with a default instantiation of these algorithms via interface.py through some simple commandline arguments:

  • --op: "train"/"test". default="train". The former tells the script to instantiate a specific agent, train it with a specific number of episodes/timesteps and save the resulting model in models/ as a .mdl file; the latter tests a given model (selected in models/ through --model) and renders the results.

  • --model: str default=None. Is just the name of the model you want to save after a training procedure or the name of the model you want to load when testing an agent. Be aware that no specific check is done to assert its compatibility with the specified --agent-type, therefore it should be coherent with said choice. Don't use --agent-type actorCritic and load a model for TRPO with --model trpo-model.mdl.

  • --device: "cpu"/"cuda". default="cpu". The whole project is developed in PyTorch [https://pytorch.org/], therefore there is the possibility to perform some operations using the GPU. At this specific state of the project such opportunity is not yet implemented but will for sure in next releases. Just leave it as default for now.

  • --render: bool. default=True. Is a boolean value used to render the environment during training or testing.

  • --episodes: int. default=10. Is the number of episodes/timesteps (for REINFORCE/ActorCritic and PPO/TRPO respectively) to train an agent or test it in a specified environemnt

  • --agent-type: "REINFORCE"/"actorCritic"/"PPO"/"TRPO". default="REINFORCE". Is a string used to select the specific agent to train/test.

  • --domain-type: "source"/"target". default="source". Is a string used to indicate the specific environment to build. Our task is a sim-to-sim job, therefore the source environemnt refers to the one which has a torso mass set to 1kg less than the target environment.

  • --batch-size: int. default=10. Is an integer used to indicate the dimension of the batch chuck used for ActorCritic policy updates.

  • --print-every: int. default=10. Is the number of episodes to skip before printing the episode's total reward during training/testing.

Just for clarity purposes, here follows an example on how to train a REINFORCE agent for 50000 episodes in the source domain and print its total reward every 100 episodes.

python interface.py --op train --agent-type reinforce --episodes 50000 --domain-type source --print-every 100

And testing it in the target domain for 50 episodes:

python interface.py --op test --agent-type reinforce --episodes 50 --domain-type target --render --model reinforce-model.mdl

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UDR

Uniform Domain Randomization is a very simple yet effective strategy to help the agent generalize its behaviour after little environment's variations. Inside domain_randomiation/, the file trpo_train.py and trpo_test.py perform the training and testing of a TRPO agent with some specific user-specified bounds. trpo_train.py takes this bounds and saves the modell, while trpo_test.py loads it. trpo_train.py takes some simple input arguments:

  • --n-samples: int. default=5. Number of training sessions.
  • --domain-type as indicated in interface.py
  • --timesteps: int. default=100000. Number of timesteps the train a TRPO agent on.
  • --mX-low/--mX-high: float. default=0.5/5. Each mass can have its specific bounds, but by default they are all the same. just use
python trpo_train.py --m1-low X --m1-high Y --m2-low Z --m2-high W --m3-low J --m3-high K

to specify different bounds for the parametric distributions from which to sample each mass.

To test the agent, the usual arguments can be specified like previously described for interace.py: n-episodes, timesteps, domain-type, render and model.

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ADR

To implement Adaptive Domain Randomization we decided to go for Bayesian Optimization [paper to reference: https://arxiv.org/pdf/2003.02471.pdf]. All the files for this specific set can be found in adaptive_dr/. BayRn.py contains all the necessary functions to perform such strategy, and can be run as a standalone file to obtaion the optimized bounds from which to sample the environments parameters (masses), or one could run (armed with some good patience) bayrn_trpo.py and let the code perform Bayesian Optimization by itself and then train and test a TRPO agent which BayRn.py output. BayRn.py allows the use of some useful arguments:

  • --n-roll: int. default=5. Number of rollouts in the target environment.
  • --min: float. default=0.25. Lower bound for masses' distribution.
  • --max: float. default=10. Higher bound for masses' distribution.
  • --maxit: int. default=15. Maximum number of Bayesiam Optimization iterations.
  • --timesteps: int. default=100000. Number of timesteps for policy algorithm training.
  • --n-init: int. default=5. Number of initialization iterations.

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TESTS

The folders step2_tests/ step3_tests/ and step4_tests/ perform the hyperparameters' tuning of the agents in different contexts.

  • step2_tests/ content optimizes REINFORE, ActorCritic, PPO and TRPO parameters for environments in which no specific randomization techniques are employed.
  • step3_tests/ does the same thing but for environments in which Uniform Domain Randomization is applied.
  • step4_tests/ performs hyperparameters' tuning for a TRPO agent that makes use of BayRn.py. The way each folder works is substantially the same: first config_setter.py creates a parameter grid object of the configurations we wanted to test and writes the resulting combinations as a json file which then each indivudal script uses. Please notice that every tests_loader_"model".py has its specific folder in which the combinations are stored and a "model"_evaluation.txt in which each combination's specific performance are recorded. Eventually, config_getter.py retrives the best one for each model and stores it inside best_config.txt. For each folder a run_hpt.sh file is present and its sufficient to run it to perform all the tests in that folder.
    !! Notice that all the results are already in this repository, so to save your time we strongly recommend not to look at run_hpt.sh, kindly !!

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VARIANT

variant/ contains the content of the alternative approach we tried to implement in order to best identify the domain's parameters to randomize. A better explanation of such procedure can be read in our work's report, therefore here we just specify the order in which variant/*.py files should be executed to read the automatically generated results:

  1. dataset_generation.py to create a specific dataset suitable for our analysis.
  2. dynamics_analysis.py in which a Random Forest Regressor performs its magic on the previously built dataset.
  3. variant_validation.py which trains a TRPO agent in the source environment where just 2 of the 3 masses are randomized according to our analysis results, and then tests it in the target domain.

To generate the dataset used for the analysis, dataset_generation.py allows some arguments to be given:

  • --samples: int. default=100. Number of training samples to consider while building the dataset.
  • --timesteps: int. default= 100000. Number of training timesteps for TRPO training.
  • --low: float. default=0.5. Lower bound of distribution.
  • --high: float. default=5. Higher bound of distribution.

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CONTACTS

Francesco Pagano [email protected]
Francesco Capuano [email protected]
Francesca Mangone [email protected]