Reinforcement learning is a subfield of machine learning in which an agent learns to make decisions by interacting with an environment to maximize cumulative reward. The parameters are: (1) an agent that selects actions, (2) an environment that provides states and rewards, (3) a policy mapping states to actions, and (4) a value function estimating long-term reward. The persistence mechanism is algorithmic — formalized through Markov decision processes, policy gradient methods, and value iteration — propagated through research literature, training frameworks, and applied systems. [formal: memoria proceduralis | substrate: behavior | horizon: a life | explicit: yes | epoch: 0.31]
Accepted ontology entry
reinforcement-learning
Reinforcement learning is a subfield of machine learning in which an agent learns to make decisions by interacting with an environment to maximize cumulative reward. The parameters are: (1) an agent that selects actions, (2) an environment…
Definition
Why it is in scope
A machine learning paradigm where an agent learns optimal behavior through interaction with an environment, receiving reward signals that guide policy updates toward maximizing cumulative future reward.
Names and aliases
- reinforcement-learningen · CANONICAL
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Operant conditioning (Skinner, 1930s) predated and directly fed into reinforcement learning. Both share the core principle of learning through reward and punishment signals, but operant conditioning in behavioral psychology came first and provided the conceptual foundation that reinforcement learning adapted into a computational framework.
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Reinforcement learning is a specific kind of machine learning — a competent speaker would call it 'a machine learning paradigm'.
Relations to this entry
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A reward signal is built and maintained for the sake of guiding reinforcement learning — it is the core mechanism by which RL agents learn which actions to take. Law 8d: the servant (reward signal) points at the master (reinforcement learning). Remove the reward signal and RL has no learning signal and stops operating.
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Policy gradient is a specific kind of reinforcement learning method — more specifically, a gradient-based RL method that optimizes policies via gradient computation on expected return. A competent practitioner calls policy gradient methods 'RL methods' or 'policy-based RL methods'.
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Reward models are built to approximate scalar reward signals that guide RL agents — their designed purpose is to further RL operation by providing learnable reward feedback.
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Reinforcement learning (1980s-90s, Sutton/Barto) predated and fed into AI alignment as a field. Alignment work builds on RL frameworks — reward signals, policy optimization, value functions — to achieve desired behavior. Per Law 7: Y (RL) existed first and fed into X (alignment).
Record identity
- Created
- Aug 5, 2026, 5:52 PM UTC
- Content hash
- 8a91bb5b61140558bad02e36afe2e06a90accf1b5492efa29ff72983f5770715