Running examples locally

This example and more are also available as Julia scripts and Jupyter notebooks.

See the how-to page for more information.

Using the Visualizer


In this example, we walk through how to use LyceumMuJoCoViz.jl to playback saved trajectories and interact with a saved policy in real time using the policy we learned in the "Learning a control policy" example.

The Code

First, let's go head and grab all the dependencies:

using LyceumAI         # For `NaturalPolicyGradient` and `DiagGaussianPolicy`
using Shapes           # For the `allocate` function
using LyceumMuJoCo     # For the HopperV2 environment
using LyceumMuJoCoViz  # For the visualizer itself
using FastClosures     # For helping avoid performance issues with closures, discussed below
using JLSO             # For loading saved data

Here we demonstrate two modes of visualizing results of an algorithm like NaturalPolicyGradient or MPPI from LyceumAI: playing back saved trajectories and interacting with a policy or controller in real time. For the former, we need only pass to the visualize function our saved trajectories as a vector of matricies, where each element of the vector is a matrix of size (length(statespace(env)), T), where T is the length the trajectory. Note that each trajectory can be of a different length. For the latter, we pass a control callback to visualize that will be called each time step!(env) is called.

function viz_hopper_NPG()
    # Load our experiment results
    x = JLSO.load("/tmp/hopper_example.jlso")

    env = LyceumMuJoCo.HopperV2()

    # Load the states from our saved trajectory, as well as the learned policy.
    states = x["stocstates"].states
    pol = x["policy"]

    # Allocate some buffers for our control callback.
    a = allocate(actionspace(env))
    o = allocate(obsspace(env))

    # As discussed in the Julia performance tips, captured variables
    # (e.g. in a closure) can sometimes hinder performance. To help with that,
    # we use `let` blocks as suggested.
    ctrlfn = let o = o, a = a, pol = pol
        function (env)
            getobs!(o, env)
            a .= pol(o)
            setaction!(env, a)

    visualize(env, controller = ctrlfn, trajectories = states)
viz_hopper_NPG (generic function with 1 method)

Now just call the function to see the visualizer appear!


You should see the following: visualizer

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