Learning to play Atari in TensorFlow using Deep Reinforcement Learning
git clone https://github.com/brendanator/atari-rl
git submodule update --init
conda create --name atari-rl python=3.5
source activate atari-rl
conda install -y -c https://conda.binstar.org/menpo opencv3
conda install -y h5py numpy
pip install tensorflow
pip install 'gym[atari]'
Python 2.7 is also supported
- Show all options -
python main.py --help - Play a specific Atari game -
python main.py --game Breakout
- ✅ Human Level Control through Deep Reinforcement Learning
python main.py
- ✅ Deep Reinforcement Learning with Double Q-learning
python main.py --double_q
- ✅ Dueling Network Architectures for Deep Reinforcement Learning
python main.py --dueling
- ✅ Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening
python main.py --optimality_tightening
- ✅ Prioritized Experience Replay
python main.py --replay_prioritized- Only proportional prioritized replay is implemented
- ✅ Unifying Count-Based Exploration and Intrinsic Motivation
python main.py --exploration_bonus
- ✅ Deep Exploration via Bootstrapped DQN
python main.py --bootstrapped
- ✅ Increasing the Action Gap: New Operators for Reinforcement Learning
python main.py --persistent_advantage_learning
- ✅ Learning values across many orders of magnitudes
python main.py --reward_scaling
- ✅ Asynchronous Methods for Deep Reinforcement Learning
python main.py --async one_steppython main.py --async n_steppython main.py --async n_step --sarsapython main.py --async a3c
- ❌ Deep Recurrent Q-Learning for Partially Observable MDPs
- ❌ Safe and efficient Off-Policy Reinforcement Learning
- ❌ Continuous Deep Q-Learning with Model-based Acceleration