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Reward in Reinforcement Learning

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Reward Function in Reinforcement Learning

The reward function formally describes the feedback an agent receives from the environment, often denoted as $R$ . Specifically, $r(s, a, s')$ represents the reward for taking action $a$ in state $s$ and transitioning to the next state $s'$ . For a sequence of state-action pairs, the reward at a specific time step $t$ is written as $r_t = r(s_t, a_t, s_{t+1})$ . In deterministic decision-making processes, where the next state $s_{t+1}$ is entirely determined by the current state $s_t$ and action $a_t$ , the notation simplifies to $r(s_t, a_t)$ .