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Update freqai-reinforcement-learning.md

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@ -58,11 +58,12 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from
Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment: Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
```python ```python
def feature_engineering_standard():
# The following features are necessary for RL models # The following features are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"] informative[f"%-raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"] informative[f"%-raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"] informative[f"%-raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"] informative[f"%-raw_low"] = informative["low"]
``` ```
Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action. Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.