The approach to optimizing the Agent policy with constraints on its behavior turned out to be promising in solving offline reinforcement learning problems. By exploiting historical transitions, the Agent policy is trained to maximize a learned value function. Behavior constrained policy can help to avoid a significant distribution shift in relation to Agent actions, which provides sufficient confidence in the assessment of the action costs. In the previous article we got acquainted ...
In the dynamic world of financial trading using of technical indicators can be of big impact. Bollinger Bands are one such effective technique that traders can use to determine possible entry and exit points based on price volatility and trend strength. This article explores automating trading choices with a Bollinger Bands trading technique utilizing the MQL5 programming language in MetaTrader 5. more...
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