Naive Advice Imitation
This repository contains the source code for the advice imitation algorithm proposed in the paper titled "Student-Initiated Action Advising via Advice Novelty".
Requirements
Packages (and their tested versions with Python 3.7):
- numpy=1.19.4
- gym=0.18.0
- tensorflow=2.2.0
- pathlib2=2.3.5
Environment:
Execution
The code can be executed simply by running
python code/main.py
The input arguments with their default values can be found in code/main.py.
The teacher agent(s) to be used can be set in code/constants.py with their saved model checkpoints as follows:
TEACHER = {
'ALE-Enduro': ('ALE24V0_EG_000_20201105-130625', '0', 6000e3),
'ALE-Freeway': ('ALE26V0_EG_000_20201105-172634', '0', 3000e3),
'ALE-Pong': ('ALE43V0_EG_000_20201106-011948', '0', 5800e3),
}where each dictionary entry uses the following format:
<Game Name>: (<Model directory>, <Model subdirectory (seed)>, <Checkpoint (timesteps)>)
We provide these example teacher models in the checkpoints directory.
Here are some example commands to run 3 types of experiments (No Advising, Early Advising, Advice Imitation) in Freeway game:
python main.py --run-id ALE26V0_NA --process-index 0 --machine-name HOME --n-training-frames 3000000 --n-evaluation-trials 10 --evaluation-period 25000 --evaluation-visualization-period 10 --dqn-gamma 0.99 --dqn-rm-type uniform --dqn-per-alpha 0.4 --dqn-per-beta 0.6 --dqn-rm-init 50000 --dqn-rm-max 500000 --dqn-target-update 7500 --dqn-batch-size 32 --dqn-learning-rate 6.25e-05 --dqn-train-per-step 1 --dqn-train-period 4 --dqn-adam-eps 0.00015 --dqn-eps-start 1.0 --dqn-eps-final 0.01 --dqn-eps-steps 500000 --dqn-huber-loss-delta 1.0 --dqn-hidden-size 512 --model-save-period 100000 --visualization-period 200 --env-name ALE-Freeway --env-training-seed 0 --env-evaluation-seed 1 --seed 103 --action-advising-method none
python main.py --run-id ALE26V0_EA --process-index 0 --machine-name HOME --n-training-frames 3000000 --n-evaluation-trials 10 --evaluation-period 25000 --evaluation-visualization-period 10 --dqn-gamma 0.99 --dqn-rm-type uniform --dqn-per-alpha 0.4 --dqn-per-beta 0.6 --dqn-rm-init 50000 --dqn-rm-max 500000 --dqn-target-update 7500 --dqn-batch-size 32 --dqn-learning-rate 6.25e-05 --dqn-train-per-step 1 --dqn-train-period 4 --dqn-adam-eps 0.00015 --dqn-eps-start 1.0 --dqn-eps-final 0.01 --dqn-eps-steps 500000 --dqn-huber-loss-delta 1.0 --dqn-hidden-size 512 --model-save-period 100000 --visualization-period 200 --env-name ALE-Freeway --env-training-seed 0 --env-evaluation-seed 1 --seed 103 --action-advising-method ea --action-advising-budget 10000
python main.py --run-id ALE26V0_AR --process-index 0 --machine-name HOME --n-training-frames 3000000 --n-evaluation-trials 10 --evaluation-period 25000 --evaluation-visualization-period 10 --dqn-gamma 0.99 --dqn-rm-type uniform --dqn-per-alpha 0.4 --dqn-per-beta 0.6 --dqn-rm-init 50000 --dqn-rm-max 500000 --dqn-target-update 7500 --dqn-batch-size 32 --dqn-learning-rate 6.25e-05 --dqn-train-per-step 1 --dqn-train-period 4 --dqn-adam-eps 0.00015 --dqn-eps-start 1.0 --dqn-eps-final 0.01 --dqn-eps-steps 500000 --dqn-huber-loss-delta 1.0 --dqn-hidden-size 512 --model-save-period 100000 --visualization-period 200 --env-name ALE-Freeway --env-training-seed 0 --env-evaluation-seed 1 --seed 103 --action-advising-method ai --action-advising-budget 10000 --bc-uc-threshold 0.01 --bc-batch-size 32 --bc-learning-rate 0.0001 --bc-adam-eps 0.00015 --bc-dropout-rate 0.2 --bc-training-iters 50000 --bc-hidden-size 512 --bc-uc-ensembles 100