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.travis.yml
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.travis.yml
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dist: trusty
language: python
python:
- 2.7
- 3.5
install:
# Install conda
- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
else
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
fi
- bash miniconda.sh -b -p $HOME/miniconda
- export PATH="$HOME/miniconda/bin:$PATH"
- hash -r
- conda config --set always_yes yes --set changeps1 no
- conda update -q conda
- conda info -a # Useful for debugging any issues with conda
# Install dependencies in conda environment
- conda create -q -n atari-rl python=$TRAVIS_PYTHON_VERSION
- source activate atari-rl
- conda install -y -c https://conda.binstar.org/menpo opencv3
- conda install h5py numpy
- pip install tensorflow gym[atari]
script:
# Tests
- python -m test.test_replay_memory
- python -m test.test_replay_priorities
# Summaries and saving/loading replay memory
- python main.py --save_replay_memory --run_dir runs/test_run_dir --summary_step_period 4 --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --load_replay_memory --run_dir runs/test_run_dir --summary_step_period 4 --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --boostrapped --game air_raid --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
# Synchronous algorithms
- python main.py --dueling --game amidar --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --double_q --game assault --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --exploration_bonus --game asterix --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --optimality_tightening --game asteroids --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --persistent_advantage_learning --game atlantis --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --replay_priorities proportional --game BankHeist --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --reward_scaling --game battle_zone --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
- python main.py --sarsa --game beam_rider --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
# All synchronous algorithms combined
- python main.py --boostrapped --dueling --double_q --exploration_bonus --optimality_tightening --persistent_advantage_learning --replay_prioritized --reward_scaling --game yars_revenge --replay_start_size 8 --replay_capacity 10000 --num_steps 8 --initial_exploration 0.5
# Asynchronous algorithms
- python main.py --async one_step --game Berzerk --num_steps 8 --num_threads 4 --initial_exploration 0.5
- python main.py --async n_step --game bowling --num_steps 8 --num_threads 4 --initial_exploration 0.5
- python main.py --async n_step --sarsa --game boxing --num_steps 8 --num_threads 4 --initial_exploration 0.5
- python main.py --async a3c --game Breakout --num_steps 8 --num_threads 4 --initial_exploration 0.5
notifications:
email: false
slack:
rooms:
secure: 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
on_success: change
on_failure: always