Using two Deep Reinforcement Learning Algorithms to solve Atari 2600 Games respectively. First: implements a Double DQN with Prioritized Experience Replay (Proportional Prioritization). Second: implements Asynchronous Advantage Actor-Critic (A3C) algorithm.
The implementation of Double DQN with Prioritized Experience Replay (Proportional Prioritization) is based on:
- Mnih et al. Human-level Control through Deep Reinforcement Learning [2015.02]
- van Hasselt et al. Deep Reinforcement Learning with Double Q-learning [2015.12]
- Schaul et al. Prioritized Experience Replay [2016.02]
The implementation of Asynchronous Advantage Actor-Critic (A3C) algorithm is based on:
- Mnih et al. Asynchronous Methods for Deep Reinforcement Learning [2016.06]
- Babaeizadeh et al. Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU [2017.03]
- Python 2.7.x or 3.6.x
- NumPy 1.13.1
- TensorFlow 1.0.* or 1.1.* or 1.2.* or 1.3.*
- Keras 2.0.8
- SciPy 0.19.1 (For image pre-processing)
- H5py 2.7.1 (For saving or loading Keras model)
- Gym 0.9.3 (Provides Atari 2600 Games)