Home / AIO Classes / MachineLearningTask / Reinforcement Learning

Reinforcement Learning

MachineLearningTask AIO:ReinforcementLearning NOT_REVIEWED

A machine learning task focused on methods that do not require labeled input/output pairs or explicit correction of sub-optimal actions focusing instead on balancing exploration and exploitation to optimize performance over time.

Ontology IRI: https://w3id.org/aio/ReinforcementLearning

Ontology Structure & Relationships

Parent Classes

AIO:MachineLearningTask (Machine Learning Task)

Branches

MachineLearningTask

Annotations & Sources

Definition Sources:

https://en.wikipedia.org/wiki/Reinforcement_learning

Subsets:

https://w3id.org/aio/MachineLearningSubset

Curated AIMech Concepts Grounded to this Class

32 Concepts

Curated Concept	Kind	Mapping Relation	Rationale
Accumulating Sarsa(lambda) `AIMech:AccumulatingSarsaLambda`	Algorithms	related_match	The selected algorithm learns action values from environmental reward while selecting exploratory actions to improve control. The pinned target is a machine-learning task emphasizing exploration/exploitation, not this update procedure; related_match avoids equivalence or a subclass assertion.
Ant-based telephone routing `AIMech:AntBasedTelephoneRouting`	Algorithms	related_match	The pinned task emphasizes feedback-driven exploration and exploitation without labeled corrective pairs. Ant-based telephone routing adapts route probabilities from ant age and congestion and can deliberately explore alternatives, supporting this qualified feedback-adaptation relationship. The local subject is a distributed routing algorithm, not every reinforcement-learning task. This is curator classification, not equivalence, an author-declared MDP or Q-learning formulation, or a convergence theorem; the report characterizes its motivating ant process as self-organization.
AntNet `AIMech:AntNet`	Algorithms	related_match	The source explicitly analyzes AntNet as adaptive stochastic routing with reinforcement-based policy updates and discusses its connection to reinforcement learning. The AIO target is a learning task; this related match does not equate AntNet to that task, to Q-learning, or to a stationary Markov controller.
Ant-Q `AIMech:AntQ`	Algorithms	related_match	Ant-Q explicitly constructs shared action-like values using a Q-learning-inspired discounted next-state term and delayed reinforcement. This relates the concrete combinatorial optimizer to the pinned learning-task class; it asserts neither identity nor subclass membership. History-dependent feasibility and source-specific update schedules prevent importing standard Q-learning guarantees.
Deep deterministic policy gradient `AIMech:DeepDeterministicPolicyGradient`	Algorithms	related_match	This algorithm implements reinforcement-learning updates. The AIO target is a learning task, not the same method or an asserted algorithm superclass.
Deep Q-network learning `AIMech:DeepQNetwork`	Algorithms	related_match	This algorithm implements reinforcement-learning updates. The AIO target is a learning task, not the same method or an asserted algorithm superclass.
Full-echo Q-routing `AIMech:FullEchoQRouting`	Algorithms	related_match	The source-scoped procedure adapts distributed routing decisions using local delivery-time feedback and neighboring bootstrapped estimates. The pinned AIO target is a broader learning-task classification; this algorithm-to-task relation asserts neither exact identity nor a subclass, stationary-MDP or convergence guarantee.
Generalized advantage estimation `AIMech:GeneralizedAdvantageEstimation`	Mathematical Methods	related_match	This estimator supplies advantages for policy-gradient learning. The AIO target is a learning task, not the same method or an asserted algorithm superclass.
Holland bucket-brigade credit assignment `AIMech:HollandBucketBrigade`	Algorithms	related_match	External payoff and inter-rule payments adapt rule strengths without requiring labeled corrective input/output pairs. This connects the credit-assignment procedure to the pinned reinforcement-learning task; it asserts neither exact identity nor subclassing of the whole task, an MDP or Q-learning specification, or an exploration guarantee.
Modified Connectionist Q-Learning `AIMech:ModifiedConnectionistQLearning`	Algorithms	related_match	The method learns action values from actions, rewards and transitions for control. The pinned AIO target is a learning task; this qualified algorithm-to-task relation is neither exact identity nor a subclass assertion, a trained model instance or a transferable convergence guarantee.
Peng and Williams Q(lambda) `AIMech:PengWilliamsQLambda`	Algorithms	related_match	The method learns action values from actions, rewards and transitions for control. The pinned AIO target is a learning task; this qualified algorithm-to-task relation is neither exact identity nor a subclass assertion, a trained model instance or a transferable convergence guarantee.
Proximal policy optimization `AIMech:ProximalPolicyOptimization`	Algorithms	related_match	PPO is a particular policy-gradient procedure within reinforcement-learning practice; the AIO class denotes the broader task.
Q-learning `AIMech:QLearning`	Algorithms	related_match	The source explicitly defines Q-learning as a model-free reinforcement-learning algorithm. The pinned AIO target is a learning task; the relation records this direct connection without equating the algorithm to the task or asserting an algorithm-class hierarchy.
Q-routing `AIMech:QRouting`	Algorithms	related_match	The primary method embeds local reinforcement-learning updates in packet routers. The AIO target is the task category, not the same algorithm, a network model, or a theorem guaranteeing adaptive network convergence.
REINFORCE `AIMech:REINFORCE`	Algorithms	related_match	The primary source explicitly develops a stochastic reinforcement-learning algorithm family driven by reward feedback. The exact AIO target denotes the broader learning task. This is an algorithm-to-task related match, not equivalence or a claim that every reinforcement learner follows this gradient rule.
Replacing eligibility traces `AIMech:ReplacingEligibilityTraces`	Mathematical Methods	related_match	A credit-assignment component used within reinforcement-learning prediction/control; the broader pinned task is neither the recurrence itself nor a guaranteed outcome.
Sarsa `AIMech:Sarsa`	Algorithms	related_match	The inspected source defines Sarsa as an on-policy reinforcement-learning control algorithm. The pinned AIO class denotes the learning task rather than this particular action-value update, so the relation is neither equivalence nor an asserted algorithm-class hierarchy.
Soft actor-critic `AIMech:SoftActorCritic`	Algorithms	related_match	SAC is a particular algorithm for policy learning, whereas the pinned AIO target is a broad machine-learning task.
Soft Q-learning `AIMech:SoftQLearning`	Algorithms	related_match	The source-defined algorithm learns action values and a reward-driven policy through interaction and replay, with an entropy term influencing exploration. This supports a qualified relation to the pinned reinforcement-learning task, not exact identity or an algorithm-to-task subclass assertion.
Stein Variational Policy Gradient `AIMech:SteinVariationalPolicyGradient`	Algorithms	related_match	In this scientific preparation, SVPG is a policy optimization algorithm serving the pinned reinforcement-learning task, which is an AIO MachineLearningTask. This algorithm-to-task relationship asserts neither equivalence nor subclass or broad-match inclusion. Parameterized policies need not use the neural architecture of the reported experiments. Root accepted this draft recommendation against the inspected historical snapshot; fresh canonical-time context remains required.
Stochastic learning automata `AIMech:StochasticLearningAutomata`	Learning Paradigms	related_match	The framework adapts action selection from evaluative environmental feedback, supporting a relationship to the pinned reinforcement-learning task. The local subject is the classical finite-action probability-update family, not every reinforcement-learning method or the task itself. This qualified related mapping asserts neither equivalence nor an upstream subclass relation.
Temporal-difference learning `AIMech:TemporalDifferenceLearning`	Learning Paradigms	related_match	TD prediction supplies value estimates for reinforcement-learning control, as directly illustrated by the author book’s Section 6.4. The local family also covers prediction without action selection. The pinned AIO target is a learning task; this connection asserts neither equivalence nor that every TD prediction problem is a control task.
True online GTD(lambda) `AIMech:TrueOnlineGTDLambda`	Algorithms	related_match	The algorithm estimates a specified policy’s state values from reward transitions generated by a potentially different behavior policy. This supplies prediction machinery used in reinforcement learning. The pinned class is a broader learning task involving exploration/performance optimization; the relation does not claim exact identity, an algorithm-to-task subclass edge, action selection, policy improvement or convergence.
True online Sarsa(lambda) `AIMech:TrueOnlineSarsaLambda`	Algorithms	related_match	The source-scoped algorithm learns state-action values from reward transitions and uses those estimates for behavior selection, with exploration such as epsilon-greedy. This supports a qualified association with the pinned reinforcement-learning task. It asserts neither exact identity nor an algorithm-to-task subclass relation, and it supplies no guarantee of exploration, convergence or optimal control.
True online TD(lambda) `AIMech:TrueOnlineTDLambda`	Algorithms	related_match	The method estimates a fixed policy’s state values from reward transitions and supplies prediction machinery used within reinforcement learning. The pinned target is a broader learning task involving exploration and performance optimization; this related association asserts neither exact identity nor that the prediction algorithm itself chooses actions, optimizes a policy or guarantees convergence.
Trust region policy optimization `AIMech:TrustRegionPolicyOptimization`	Algorithms	related_match	This algorithm implements reinforcement-learning updates. The AIO target is a learning task, not the same method or an asserted algorithm superclass.
Usage-based TD(lambda) `AIMech:UsageBasedTDLambda`	Algorithms	related_match	The algorithm estimates state values from on-policy reward transitions and supplies prediction machinery used in reinforcement learning. The pinned target is a broader exploration/performance-oriented learning task; this is a qualified association, not exact identity, a subclass edge, an action-selection procedure or a policy-improvement guarantee.
Usage-based true online TD(lambda) `AIMech:UsageBasedTrueOnlineTDLambda`	Algorithms	related_match	The algorithm estimates state values from on-policy reward transitions and supplies prediction machinery used in reinforcement learning. The pinned target is a broader exploration/performance-oriented learning task; this is a qualified association, not exact identity, a subclass edge, an action-selection procedure or a policy-improvement guarantee.
WIS-GTD(lambda) `AIMech:WISGTDLambda`	Algorithms	related_match	The algorithm estimates target-policy state values from reward transitions sampled under a behavior policy and supplies prediction machinery used in reinforcement learning. The pinned target is a broader exploration/performance-oriented learning task; this is a qualified association, not exact identity, a subclass edge, action selection, policy improvement or convergence.
WIS-TD(lambda) `AIMech:WISTDLambda`	Algorithms	related_match	The algorithm estimates policy state values from reward transitions, potentially sampled under a different behavior policy, and supplies prediction machinery used in reinforcement learning. The pinned class is a broader learning task; this is a qualified association, not exact identity, an algorithm-to-task subclass edge, action selection, policy improvement or convergence.
WIS-TO-GTD(lambda) `AIMech:WISTrueOnlineGTDLambda`	Algorithms	related_match	The algorithm predicts target-policy state values from reward transitions sampled under a behavior policy, supplying prediction machinery used in reinforcement learning. The pinned AIO target describes a broader exploration/performance-oriented task. This is a qualified association, not exact identity, a subclass assertion, action selection, policy improvement or convergence.
Watkins hard-cut multistep Q-learning `AIMech:WatkinsHardCutMultistepQLearning`	Algorithms	related_match	The procedure learns action values and an implicit control policy from actions, rewards and transitions. The pinned AIO class denotes a learning task; this algorithm-to-task relation asserts neither exact identity nor a subclass relation or convergence guarantee.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub

Release Version: aio.json

Commit: 397851c547…

License: CC-BY-4.0

Imported At: 2026-10-10T07:34:10Z

SHA256: 8f741d71408c…

Source File: data/aio/ReinforcementLearning.yaml