AI Thought Leadership · Individual

Richard Sutton

Temporal-difference learning, Dyna, reinforcement learning, continual experience.

Type

Individual (Individual researcher)

Directory section

Additional person profiled

What to monitor

Not stated in the source directory

Directory last reviewed

August 16, 2026

Why this source is monitored

Temporal-difference learning, Dyna, reinforcement learning, continual experience.

Topics. ML foundations; agents; world models

Official links

Personal site (opens in a new tab)

Caveat and non-endorsement

Personal site. Foundational primary papers should support historical claims.

Inclusion in this directory is a monitoring decision by Implement Agentic. It is not an endorsement, a partnership, a client relationship, or a claim that this person or organization endorses us. Product claims and personal forecasts must be traced to technical evidence before they are used as fact.

How we weight this kind of source

Primary research: papers, technical reports, system cards, benchmarks, official repositories, university research, or government evaluations.
Open ecosystem: code, weights, datasets, model cards, evaluation tooling, and reproducibility work. Verify license and provenance separately.
Expert synthesis: interpretation by a named researcher or practitioner. Link through to the underlying evidence.
Market signal: startup, investor, or portfolio activity. Use to detect direction of interest, not as proof of capability.
Publication/index: discovery infrastructure. A listing establishes publication or preprint status, not correctness.