pyRDDLGym-jax

pyRDDLGym-jax (known in the literature as JaxPlan) is an efficient gradient-based/differentiable planning algorithm in JAX. It provides:

1. automatic translation of any RDDL description file into a differentiable simulator in JAX
2. flexible policy class representations, automatic model relaxations for working in discrete and hybrid domains, and Bayesian hyper-parameter tuning.

Some demos of solved problems by JaxPlan:

Warning

Starting in version 1.0 (major release), the weight parameter in the config file was removed, and was moved to the individual logic components which have their own unique weight parameter assigned. Furthermore, the tuning module has been redesigned from the ground up, and supports tuning arbitrary hyper-parameters via config templates! Finally, the terrible visualizer for the planner was removed and replaced with an interactive real-time dashboard (similar to tensorboard, but custom designed for the planner)!

Note

While JaxPlan can support some discrete state/action problems through model relaxations, on some discrete problems it can perform poorly (though there is an ongoing effort to remedy this!). If you find it is not making sufficient progress, check out the PROST planner (for discrete spaces) or the deep reinforcement learning wrappers.

Contents

• Installation
• Running from the Command Line
• Running from Another Python Application
• Configuring the Planner
• JaxPlan Dashboard
• Tuning the Planner
• Simulation
• Citing JaxPlan

Installation

To install the bare-bones version of JaxPlan with minimum installation requirements:

pip install pyRDDLGym-jax

To install JaxPlan with the automatic hyper-parameter tuning and rddlrepository:

pip install pyRDDLGym-jax[extra]

(Coming soon to pip) To install JaxPlan with the visualization dashboard:

pip install pyRDDLGym-jax[dashboard]

(Coming soon to pip) To install JaxPlan with all options:

pip install pyRDDLGym-jax[extra,dashboard]

Running from the Command Line

A basic run script is provided to run JaxPlan on any domain in rddlrepository from the install directory of pyRDDLGym-jax:

python -m pyRDDLGym_jax.examples.run_plan <domain> <instance> <method> <episodes>

where:

• domain is the domain identifier as specified in rddlrepository (i.e. Wildfire_MDP_ippc2014), or a path pointing to a valid domain.rddl file
• instance is the instance identifier (i.e. 1, 2, ... 10), or a path pointing to a valid instance.rddl file
• method is the planning method to use (i.e. drp, slp, replan)
• episodes is the (optional) number of episodes to evaluate the learned policy.

The method parameter supports three possible modes:

• slp is the basic straight line planner described in this paper
• drp is the deep reactive policy network described in this paper
• replan is the same as slp except the plan is recalculated at every decision time step.

For example, the following will train JaxPlan on the Quadcopter domain with 4 drones:

python -m pyRDDLGym_jax.examples.run_plan Quadcopter 1 slp

Running from Another Python Application

To run JaxPlan from within a Python application, refer to the following example:

import pyRDDLGym
from pyRDDLGym_jax.core.planner import JaxBackpropPlanner, JaxOfflineController

# set up the environment (note the vectorized option must be True)
env = pyRDDLGym.make("domain", "instance", vectorized=True)

# create the planning algorithm
planner = JaxBackpropPlanner(rddl=env.model, **planner_args)
controller = JaxOfflineController(planner, **train_args)

# evaluate the planner
controller.evaluate(env, episodes=1, verbose=True, render=True)
env.close()

Here, we have used the straight-line controller, although you can configure the combination of planner and policy representation if you wish. All controllers are instances of pyRDDLGym's BaseAgent class, so they provide the evaluate() function to streamline interaction with the environment. The **planner_args and **train_args are keyword argument parameters to pass during initialization, but we strongly recommend creating and loading a config file as discussed in the next section.

Configuring the Planner

The simplest way to configure the planner is to write and pass a configuration file with the necessary hyper-parameters. The basic structure of a configuration file is provided below for a straight-line planner:

[Model]
logic='FuzzyLogic'
comparison_kwargs={'weight': 20}
rounding_kwargs={'weight': 20}
control_kwargs={'weight': 20}

[Optimizer]
method='JaxStraightLinePlan'
method_kwargs={}
optimizer='rmsprop'
optimizer_kwargs={'learning_rate': 0.001}

[Training]
key=42
epochs=5000
train_seconds=30

The configuration file contains three sections:

• [Model] specifies the fuzzy logic operations used to relax discrete operations to differentiable approximations; the weight dictates the quality of the approximation, and tnorm specifies the type of fuzzy logic for relacing logical operations in RDDL (e.g. ProductTNorm, GodelTNorm, LukasiewiczTNorm)
• [Optimizer] generally specify the optimizer and plan settings; the method specifies the plan/policy representation (e.g. JaxStraightLinePlan, JaxDeepReactivePolicy), the gradient descent settings, learning rate, batch size, etc.
• [Training] specifies computation limits, such as total training time and number of iterations, and options for printing or visualizing information from the planner.

For a policy network approach, simply change the [Optimizer] settings like so:

...
[Optimizer]
method='JaxDeepReactivePolicy'
method_kwargs={'topology': [128, 64], 'activation': 'tanh'}
...

The configuration file must then be passed to the planner during initialization. For example, the previous script here can be modified to set parameters from a config file:

from pyRDDLGym_jax.core.planner import load_config

# load the config file with planner settings
planner_args, _, train_args = load_config("/path/to/config.cfg")
    
# create the planning algorithm
planner = JaxBackpropPlanner(rddl=env.model, **planner_args)
controller = JaxOfflineController(planner, **train_args)
...

JaxPlan Dashboard

Since version 1.0, JaxPlan has an optional dashboard that allows keeping track of the planner performance across multiple runs, and visualization of the policy or model, and other useful debugging features.

To run the dashboard, add the following entry to your config file:

...
[Training]
dashboard=True
...

More documentation about this and other new features will be coming soon.

Tuning the Planner

It is easy to tune the planner's hyper-parameters efficiently and automatically using Bayesian optimization. To do this, first create a config file template with patterns replacing concrete parameter values that you want to tune, e.g.:

[Model]
logic='FuzzyLogic'
comparison_kwargs={'weight': TUNABLE_WEIGHT}
rounding_kwargs={'weight': TUNABLE_WEIGHT}
control_kwargs={'weight': TUNABLE_WEIGHT}

[Optimizer]
method='JaxStraightLinePlan'
method_kwargs={}
optimizer='rmsprop'
optimizer_kwargs={'learning_rate': TUNABLE_LEARNING_RATE}

[Training]
train_seconds=30
print_summary=False
print_progress=False
train_on_reset=True

would allow to tune the sharpness of model relaxations, and the learning rate of the optimizer.

Next, you must link the patterns in the config with concrete hyper-parameter ranges the tuner will understand:

import pyRDDLGym
from pyRDDLGym_jax.core.tuning import JaxParameterTuning, Hyperparameter

# set up the environment   
env = pyRDDLGym.make(domain, instance, vectorized=True)
    
# load the config file template with planner settings
with open('path/to/config.cfg', 'r') as file: 
    config_template = file.read() 
    
# map parameters in the config that will be tuned
def power_10(x):
    return 10.0 ** x
    
hyperparams = [
    Hyperparameter('TUNABLE_WEIGHT', -1., 5., power_10),  # tune weight from 10^-1 ... 10^5
    Hyperparameter('TUNABLE_LEARNING_RATE', -5., 1., power_10),   # tune lr from 10^-5 ... 10^1
]
    
# build the tuner and tune
tuning = JaxParameterTuning(env=env,
                            config_template=config_template,
                            hyperparams=hyperparams,
                            online=False,
                            eval_trials=trials,
                            num_workers=workers,
                            gp_iters=iters)
tuning.tune(key=42, log_file='path/to/log.csv')

A basic run script is provided to run the automatic hyper-parameter tuning for the most sensitive parameters of JaxPlan:

python -m pyRDDLGym_jax.examples.run_tune <domain> <instance> <method> <trials> <iters> <workers>

where:

• domain is the domain identifier as specified in rddlrepository
• instance is the instance identifier
• method is the planning method to use (i.e. drp, slp, replan)
• trials is the (optional) number of trials/episodes to average in evaluating each hyper-parameter setting
• iters is the (optional) maximum number of iterations/evaluations of Bayesian optimization to perform
• workers is the (optional) number of parallel evaluations to be done at each iteration, e.g. the total evaluations = iters * workers.

Simulation

The JAX compiler can be used as a backend for simulating and evaluating RDDL environments:

import pyRDDLGym
from pyRDDLGym.core.policy import RandomAgent
from pyRDDLGym_jax.core.simulator import JaxRDDLSimulator

# create the environment
env = pyRDDLGym.make("domain", "instance", backend=JaxRDDLSimulator)

# evaluate the random policy
agent = RandomAgent(action_space=env.action_space, num_actions=env.max_allowed_actions)
agent.evaluate(env, verbose=True, render=True)

For some domains, the JAX backend could perform better than the numpy-based one, due to various compiler optimizations. In any event, the simulation results using the JAX backend should (almost) always match the numpy backend.

Citing JaxPlan

The following citation describes the main ideas of JaxPlan. Please cite it if you found it useful:

@inproceedings{gimelfarb2024jaxplan,
    title={JaxPlan and GurobiPlan: Optimization Baselines for Replanning in Discrete and Mixed Discrete and Continuous Probabilistic Domains},
    author={Michael Gimelfarb and Ayal Taitler and Scott Sanner},
    booktitle={34th International Conference on Automated Planning and Scheduling},
    year={2024},
    url={https://openreview.net/forum?id=7IKtmUpLEH}
}

Some of the implementation details derive from the following literature, which you may wish to also cite in your research papers:

• A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs
• Deep reactive policies for planning in stochastic nonlinear domains, AAAI 2019
• Scalable planning with tensorflow for hybrid nonlinear domains, NeurIPS 2017