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 ...
Offline reinforcement learning allows the training of models based on data collected from interactions with the environment. This allows a significant reduction of the process of interacting with the environment. Moreover, given the complexity of environmental modeling, we can collect real-time data from multiple research agents and then train the model using this data. At the same time, using a static training dataset significantly reduces the environment information available to us. ...
We continue the development of multi-symbol, multi-period indicators which we started in the previous article. A single-color indicator buffer is a regular double array, which is filled with data when calculating the indicator. We can obtain data from this array and display it on a chart using the CopyBuffer() function provided that the receiving array will be a double array set as an indicator's plotting buffer (SetIndexBuffer()). When copying data from the buffer of the calculated ...
IntroductionDeal classPosition classHistorical position list classPosition profit graph indicatorConclusion more...
The main advantage of relational models is the ability to build dependencies between objects. That enables the structuring of the source data. The relational model can be represented in the form of graphs, in which objects and events are represented as nodes, while relationships show dependencies between objects and events. more...
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