Code for NeurIPS 2021 paper "Curriculum Offline Imitation Learning"

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Deep Learning COIL
Overview

README

The code is based on the ILswiss.

To run the code, use

python run_experiment.py --nosrun -e <your YAML file> -g <gpu id>

Generally, run_experiment.py loads the YAML file, creating multiple processes, each of which runs the script assigned in the YAML file.

The script of COIL is run_scripts/coil_script.py. Dataset settings are in demos_listing.yaml. The core algorithm is in rlkit/torch/coil/coil.py. New algorithms should also be put under similar directories. A trajectory replay buffer and the trajectory picking algorithm is in rlkit/data_management/episodic_replay_buffer_coil.py.

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Comments
  • Fail to run COIL

    Fail to run COIL

    1. running demo_spec.yaml

    File "run_scripts/coil_script.py", line 167, in experiment(exp_specs, snapshot=snapshot, extra_data=extra_data) File "run_scripts/coil_script.py", line 57, in experiment env_specs['eval_env_seed'], variant['coil_params']['discount'], KeyError: 'discount'

    1. running hc_md.yaml

    File "run_experiment.py", line 36, in for variant in vg_fn(): File "/COIL/rlkit/launchers/launcher_util.py", line 457, in vg_fn dict_to_yield = add_variable_to_constant_specs(constants, flat_variables) File "/COIL/rlkit/launchers/launcher_util.py", line 428, in add_variable_to_constant_specs cur_sub_dict = cur_sub_dict[sub_key] KeyError: 'coil_params'

    opened by KeysaYoung 2
Owner
ApexRL
RL group @ ApexLab in SJTU. Focusing on reinforcement learning, multi-agent learning and related applications.
ApexRL
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