The ML-Agents toolkit, the part that allows you to train DRL agents, is considered one of the more serious and top-end frameworks for training agents. Since the framework was developed on top of Unity, it tends to perform better on Unity-like environments. However, not unlike many others who spend time training agents, the Unity developers realized early on that some environments present such difficult challenges as to require us to assist our agents.
Now, this assistance is not so much direct but rather indirect and often directly relates to how easy or difficult it is for an agent to find rewards. This, in turn, directly relates to how well the environment designer can build a reward function that an agent can use to learn an environment. There are also the times when an environment's state space is so large and not obvious that creating a typical...
