cli.rst

Command Line Interface

d3rlpy provides the convenient CLI tool.

plot

Plot the saved metrics by specifying paths:

$ d3rlpy plot <path> [<path>...]

options

option	description
--window	moving average window.
--show-steps	use iterations on x-axis.
--show-max	show maximum value.
--label	label in legend.
--xlim	limit on x-axis (tuple).
--ylim	limit on y-axis (tuple).
--title	title of the plot.
--save	flag to save the plot as an image.

example:

$ d3rlpy plot d3rlpy_logs/CQL_20201224224314/environment.csv

plot-all

Plot the all metrics saved in the directory:

$ d3rlpy plot-all <path>

example:

$ d3rlpy plot-all d3rlpy_logs/CQL_20201224224314

export

Export the saved model to the inference format, onnx and torchscript:

$ d3rlpy export <path>

options

option	description
--format	model format (torchscript, onnx).
--params-json	explicitly specify params.json.
--out	output path.

example:

$ d3rlpy export d3rlpy_logs/CQL_20201224224314/model_100.pt

record

Record evaluation episodes as videos with the saved model:

$ d3rlpy record <path> --env-id <environment id>

options

option	description
--env-id	Gym environment id.
--env-header	arbitrary Python code to define environment to evaluate.
--out	output directory.
--params-json	explicitly specify params.json
--n-episodes	the number of episodes to record.
--frame-rate	video frame rate.
--record-rate	images are recored every record-rate frames.
--epsilon	ϵ-greedy evaluation.

example:

# record simple environment
$ d3rlpy record d3rlpy_logs/CQL_20201224224314/model_100.pt --env-id HopperBulletEnv-v0

# record wrapped environment
$ d3rlpy record d3rlpy_logs/Discrete_CQL_20201224224314/model_100.pt \
    --env-header 'import gym; from d3rlpy.envs import Atari; env = Atari(gym.make("BreakoutNoFrameskip-v4"), is_eval=True)'

play

Run evaluation episodes with rendering:

$ d3rlpy play <path> --env-id <environment id>

options

option	description
--env-id	Gym environment id.
--env-header	arbitrary Python code to define environment to evaluate.
--params-json	explicitly specify params.json
--n-episodes	the number of episodes to run.

example:

# record simple environment
$ d3rlpy play d3rlpy_logs/CQL_20201224224314/model_100.pt --env-id HopperBulletEnv-v0

# record wrapped environment
$ d3rlpy play d3rlpy_logs/Discrete_CQL_20201224224314/model_100.pt \
    --env-header 'import gym; from d3rlpy.envs import Atari; env = Atari(gym.make("BreakoutNoFrameskip-v4"), is_eval=True)'