Flow supports visualization of both rllab and RLlib computational experiments. When using one of the below visualizers, a window will appear similar to the one in the figure below. Click on the play button (highlighted in red) and the simulation will begin, with the autonomous vehicles exhibiting the behavior trained by the reinforcement learning algorithm.



Call the rllab visualizer with

python ./ /result_dir/itr_XXX.pkl

The rllab visualizer also takes some inputs:

  • --num_rollouts
  • --plotname
  • --use_sumogui
  • --run_long
  • --emission_to_csv

The params.pkl file can be used as well.


Call the RLlib visualizer with

python ./ /ray_results/result_dir 1
# OR
python ./ /ray_results/result_dir 1 --run PPO
# OR
python ./ /ray_results/result_dir 1 --run PPO \
    --module cooperative_merge --flowenv TwoLoopsMergePOEnv \
    --exp_tag cooperative_merge_example

The first command-line argument corresponds to the directory containing experiment results (usually within RLlib’s ray_results). The second is the checkpoint number, corresponding to the iteration number you wish to visualize. The --run input is optional; the default algorithm used is PPO. If the experiment module, Flow environment name, and experiment tag have not been stored automatically (see section below), then those parameters can be passed in using the flags --module, --flowenv, and --exp_tag.

Parameter storage

RLlib doesn’t automatically store all parameters needed for restoring the state of a Flow experiment upon visualization. As such, Flow experiments in RLlib include code to store relevant parameters. Include the following code snippet in RLlib experiments you will need to visualize

# Logging out flow_params to ray's experiment result folder
json_out_file = alg.logdir + '/flow_params.json'
with open(json_out_file, 'w') as outfile:
    json.dump(flow_params, outfile, cls=NameEncoder, sort_keys=True, indent=4)

These lines should be placed after initialization of the PPOAgent RL algorithm as it relies on alg.logdir. Store parameters before training, though, so partially-trained experiments can be visualized.

Another thing to keep in mind is that Flow parameters in RLlib experiments should be defined outside of the make_create_env function. This allows that environment creator function to use other experiment parameters later, upon visualization.