One of the things that often pushes us to success, or pushes us to learn, is failure. As humans, when we fail, one of two things happens: we try harder or we quit. Interestingly, this is not unlike a negative reward in reinforcement learning. In RL, an agent that gets a negative reward may quit exploring a path if it sees no future value, or that it predicts will not give enough benefit. However, if the agent feels like more exploration is needed, or it hasn't exhausted the path fully, it will push on and, often, this leads it to the right path. Again, this is certainly not unlike us humans. Therefore, in this section, we are going to train one of the more difficult example agents to push ourselves to learn how to fail and fix training failures.
Exploring the training environment
Unity is currently in the process of building a multi-level bench marking tower environment...
