* Vector Observations should include all variables relevant to allowing the agent to take the optimally informed decision.
* In cases where Vector Observations need to be remembered or compared over time, increase the `Stacked Vectors` value to allow the agent to keep track of multiple observations into the past.
* Categorical variables such as type of object (Sword, Shield, Bow) should be encoded in one-hot fashion (i.e. `3` -> `0, 0, 1`).
* Besides encoding non-numeric values, all inputs should be normalized to be in the range 0 to +1 (or -1 to 1). For example, the `x` position information of an agent where the maximum possible value is `maxValue` should be recorded as `AddVectorObs(transform.position.x / maxValue);` rather than `AddVectorObs(transform.position.x);`. See the equation below for one approach of normalization.
* Positional information of relevant GameObjects should be encoded in relative coordinates wherever possible. This is often relative to the agent position.
* `Space Type` - Corresponds to whether the observation vector contains a single integer (Discrete) or a series of real-valued floats (Continuous).
* `Space Size` - Length of vector observation for brain (In _Continuous_ space type). Or number of possible values (in _Discrete_ space type).
* `Stacked Vectors` - The number of previous vector observations that will be stacked before being sent to the brain.
* `Stacked Vectors` - The number of previous vector observations that will be stacked and used collectively for decision making. This results in the effective size of the vector observation being passed to the brain being: _Space Size_ x _Stacked Vectors_.
* `Visual Observations` - Describes height, width, and whether to grayscale visual observations for the Brain.
* `Vector Action`
* `Space Type` - Corresponds to whether action vector contains a single integer (Discrete) or a series of real-valued floats (Continuous).
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