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3 votes

One to one relation between state + action -> reward

Maybe not clear from the other answer, but no, the reward depends usually also on the state you end up, which is not deterministic by definition of MDPs, and even in that case, the reward can be noisy....
Alberto's user avatar
  • 2,153
3 votes

One to one relation between state + action -> reward

The RL framework assumes only that the reward function depends on the current state, the selected action, and the next state: $$\mathcal{R}(s_t, a_t, s_{t+1})$$ It can be deterministic but it can also ...
pi-tau's user avatar
  • 805
1 vote
Accepted

What is the name of the reward function that utilizes the rewards of the next n steps?

Your function could be called the truncated return - i.e. the sum of rewards up to some time step in the future. It would be unusual to perform reward shaping by taking the orginal reward from an ...
Neil Slater's user avatar
  • 32.4k

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