An AI agent was trained from scratch using a Double DQN algorithm to play a side-scrolling runner game. Over 76 hours and 226 million steps on a laptop CPU, the agent learned to outperform a human-designed player on harder levels. Interestingly, two custom mechanisms designed to improve learning from failure actually hindered performance, while the simplest mechanism proved most crucial. The agent also showed a tendency to lose its learning ability over time, a phenomenon predicted by recent research, though fixing this did not always improve gameplay. AI
IMPACT Demonstrates the effectiveness of basic reinforcement learning techniques and highlights potential pitfalls of over-complicating learning mechanisms.
RANK_REASON The item describes a custom implementation of a reinforcement learning agent and its training process, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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