Core reinforcement-learning framing in which actions affect future states and outcomes, including distinctions from one-step prediction or decision settings.
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12 articles in this tag.
A behavior-cloned agent can look flawless on training data and fall apart the moment it makes one small mistake. That is not bad luck. It is the signature…
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A cloned policy can score 98% on held-out demonstration states and still walk off a cliff in four seconds. The metric and the rollout are measuring two…
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Picture a system that picks which article to show a returning reader. It looks at what it knows about them—their reading history, the time of day, the…
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You can follow the agent-environment loop: the agent sees a state, picks an action, gets a reward, and lands in a new state. Then it happens again. And…
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Most beginners assume the algorithm is the hard part of reinforcement learning. Pick the right one, tune it well, and the agent will learn. Then they spend…
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The agent-environment loop feels simple until you try to make an algorithm learn. Then the loop stops being enough. The agent needs to know what it can…
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You understand what an MDP is. Then someone hands you a task description and a blank file, and the understanding evaporates.
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If you have worked through policy gradients and deep RL, the language-model post-training landscape looks like a different discipline. RLHF. PPO. DPO.…
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Most people start reinforcement learning the wrong way. They install a deep RL library, copy a DQN or PPO example, watch the agent fail—or worse, behave…
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You're halfway through a tutorial when a sentence stops you cold: "The agent updates its Q-value using the discounted return from the trajectory." You know…
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A reward is not a delayed label. That single confusion sits behind most of the trouble beginners have telling supervised learning and reinforcement…
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That single distinction separates reinforcement learning from every other branch of machine learning—and it explains why these systems learn the way they…
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