Python-predict-agent

TODO

• Shorter term
  • Train code abstraction (from DML to real)
• Longer term
  • CircularEnvironment
  • Continuous action
  • Implement VBN-like, curiosity-like, world-model-like
  • Makefile setting (ref. https://kimjingo.tistory.com/203)
  • Theoretical proof of convergence

main.py

• Train

python3 main.py --config configs/test.yaml

Deepmind lab

Download and install deepmind lab

git clone https://github.com.deepmind/lab.git

Build it following the build instructions

Clone repo inside the lab directory

cd lab
git clone https://github.com/khhandrea/python-predictive-agent

Add following lines at the end of lab/BUILD file

package(default_visibility = ["//visibility:public"])

py_library(
    name = "prednav-lib",
    srcs = glob(["python_predictive_agent/**/*.py"]),
    data = [":deepmind_lab.so"],
    imports = ["python_predictive_agent"]
)

filegroup(
    name = "prednav-filegroup",
    srcs = glob(["python_predictive_agent/configs/*.yaml"])
)

py_binary(
    name = "prednav",
    srcs = ["python_predictive_agent/main_dml.py"],
    data = [
        ":deepmind_lab.so",
        ":prednav-filegroup"
    ],
    main = "python_predictive_agent/main.py",
    deps = [
        ":prednav-lib",
    ]
)

Run the bazel command to run the agent

bazel run :prednav

or from user windows

bazel run :prednav --define graphics=sdl

Results

name	description
config.yaml	configuration file of the experiment
checkpoints/	model parameters, the form of state_dict
coordinates/	agent trajectories, the form of csv
log/	tensorboard log

Tensorboard

tensorboard --logdir=experiment_results(/{experiment name})