From a02da08065a305ed822c5795befff39afdbc97c3 Mon Sep 17 00:00:00 2001 From: Emre Date: Sun, 27 Nov 2022 22:23:00 +0300 Subject: [PATCH] Fix typo --- docs/freqai-reinforcement-learning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/freqai-reinforcement-learning.md b/docs/freqai-reinforcement-learning.md index ae3f67ed1..226c02919 100644 --- a/docs/freqai-reinforcement-learning.md +++ b/docs/freqai-reinforcement-learning.md @@ -95,7 +95,7 @@ Most of the function remains the same as for typical Regressors, however, the fu informative[f"%-{pair}raw_low"] = informative["low"] ``` -Finally, there is no explicit "label" to make - instead the you need 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 the user need 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. After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy: