Reinforcement Learning: Training Agents to Maximize Rewards Through Trial-and-Error Interactions
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Reinforcement learning involves developing an agent that learns an optimal strategy through trial and error interaction with its environment. Key concepts include states, actions, rewards.
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The agent seeks to maximize cumulative reward. Discounted cumulative reward prioritizes short-term rewards.
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Policies map states to action probabilities. Value functions estimate expected cumulative reward from states or state-action pairs.
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Bellman equations recursively relate value of current and next state, forming core of many RL algorithms.
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Finding optimal policies analytically is often impractical. RL methods approximately calculate near-optimal policies more efficiently.