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_Example of a mathematics curriculum. Lessons progress from simpler topics to |
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more complex ones, with each building on the last._ |
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When we think about how reinforcement learning actually works, the learning |
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signal is reward received occasionally throughout training. The starting point |
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When we think about how reinforcement learning actually works, the learning reward |
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signal is received occasionally throughout training. The starting point |
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when training an agent to accomplish this task will be a random policy. That |
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starting policy will have the agent running in circles, and will likely never, |
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or very rarely achieve the reward for complex environments. Thus by simplifying |
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done alone. Examples include environments where each agent only has access to |
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partial information, which needs to be shared in order to accomplish the task |
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or collaboratively solve a puzzle. |
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- Competitive Multi-Agent. Multiple interacting s with inverse reward |
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- Competitive Multi-Agent. Multiple interacting agents with inverse reward |
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scenario, s must compete with one another to either win a competition, or |
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scenario, agents must compete with one another to either win a competition, or |
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- Ecosystem. Multiple interacting s with independent reward signals linked |
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- Ecosystem. Multiple interacting agents with independent reward signals linked |
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to either a single or multiple different Brains. This scenario can be thought |
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of as creating a small world in which animals with different goals all |
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interact, such as a savanna in which there might be zebras, elephants and |
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