January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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Reinforcement learning 101
Reinforcement learning is described by an agent getting inputs of the observation and reward from the previous time-step and producing output as an action with the goal of maximizing cumulative rewards.
The agent has a policy, value function, and model:
- The algorithm used by the agent to pick the next action is known as the policy. In the previous section, we wrote a policy that would take a set of parameters theta and would return the next action based on the multiplication between the observation and the parameters. The policy is represented by the following equation:
,
S is set of states and A is set of actions. ...
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