Code for NeurIPS 2021 paper "Curriculum Offline Imitation Learning"

ApexRL

Last update: Mar 19, 2022

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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
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'

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.

GitHub

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Code for NeurIPS 2021 paper "Curriculum Offline Imitation Learning"

Related tags

Overview

README

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Comments

Fail to run COIL

Owner

ApexRL

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

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Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

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PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".