From Deep Q-learning to, A3C, policies evolved with genetic algorithms, they all fail to learn Asteroids, or at least perform way worse than human. From the hardest Atari games according to RL most of the focus is on Montezuma's revenge, which clearly suffers from sparse rewards. However I don't think this is the case of Asteroids (video), since for every asteroid shot a reward is provided. Why DRL performs that bad then?
Here are some papers that report a bad result on Asteroids (some articles refer to each other):
- Human-level control through deep reinforcement learning
- Massively Parallel Methods for Deep Reinforcement Learning
- Deep Reinforcement Learning with Double Q-learning
- Dueling Network Architectures for Deep Reinforcement Learning
- Prioritized Experience Replay
- Rainbow: Combining Improvements in Deep Reinforcement Learning
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning
- A Neuroevolution Approach to General Atari Game Playing