Entropy loss for reinforcement learning

Reinforcement learning agents are notoriously unstable to train compared to other types of machine learning algorithms. One of the ways that a reinforcement learning algorithm can underperform is by becoming stuck during training on a strategy that is neither a good solution nor the absolute worst solution. We generally refer to this phenomenon as reaching a “local minimum” in the space of game strategies.