From 5b5859238b45795852d06ef6bc7d82681b9dee6a Mon Sep 17 00:00:00 2001 From: Emre Date: Sun, 27 Nov 2022 22:06:14 +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 741a9bbb4..ae3f67ed1 100644 --- a/docs/freqai-reinforcement-learning.md +++ b/docs/freqai-reinforcement-learning.md @@ -24,7 +24,7 @@ The framework is built on stable_baselines3 (torch) and OpenAI gym for the base ### Important considerations -As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL trading environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks such as `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free-will to learn the policy (read: stoploss, take profit, ect) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world. +As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL trading environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks such as `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free-will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world. ## Running Reinforcement Learning