Deep Q-Learning Lite
Deep reinforcement learning for environments with small state spaces.
Dependencies
- NumPy
- OpenAI Gym 0.8
- TensorFlow 1.0
Learning Environment
Uses environments provided by OpenAI Gym.
Network Architecture
The network has a single hidden layer with 40 rectified linear units. The output layer has as many nodes as there are actions. Each output node represents the expected utility of an action.
Acknowledgements
Heavily influenced by DeepMind's seminal paper 'Playing Atari with Deep Reinforcement Learning' (Mnih et al., 2013) and 'Human-level control through deep reinforcement learning' (Mnih et al., 2015).