算法類外文資料翻譯-其他專業(yè)-wenkub
2023-01-30 01:06:59 本頁面
【正文】 F 0 0 100 The minus sign in the table says that the row state has no action to go to column state. For example, State A cannot go to state B (because no door connecting room A and B, remember?) In the previous sections of this tutorial, we have modeled the environment and the reward system for our agent. This section will describe learning algorithm called Q learning (which is a simplification of reinforcement learning). We have model the environment reward system as matrix R. 5 Now we need to put similar matrix name Q in the brain of our agent that will represent the memory of what the agent have learned through many experiences. The row of matrix Q represents current state of the agent, the column of matrix Q pointing to the action to go to the next state. In the beginning, we say that the agent know nothing, thus we put Q as zero matrix. In this example, for the simplicity of explanation, we assume the number of state is known (to be six). In more general case, you can start with zero matrix of single cell. It is a simple task to add more column and rows in Q matrix if a new state is found. The transition rule of this Q learning is a very simple formula The formula above have meaning that the entry value in matrix Q (that is row represent state and column represent action) is equal to corresponding entry of matrix R added by a multiplication of a learning parameter and maximum value of Q for all action in the next state. Our virtual agent will learn through experience without teacher (this is called unsupervised learning). The agent will explore state to state until it reaches the goal. We call each exploration as an episode . In one episode the agent
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