What is the 'Deadly Triad' in reinforcement learning?

The deadly triad is function approximation, bootstrapping, and off-policy learning.

When these three components are combined, training instability and divergence can occur in Q-learning. Deep RL methods carefully balance these components using replay buffers, target networks, and regularization techniques.