Atari games (as an RL testbed) — is the proposed testbed for → Semi-supervised reinforcement learning

Atari is proposed as the toy domain for semi-supervised RL specifically because it lets the paper stage a sequence of increasingly demanding oversight-scarcity conditions inside one familiar environment. The base version has the agent learn from a small number of direct reward signals; harder variants let the agent modify the displayed score without touching the real score, require it to pause the game to see its score at all, force it to infer reward from indirect cues like sounds, or reduce it to answering a handful of explicit queries such as 'how many points did I get when that enemy ship blew up?' (concrete-problems, §"5 Scalable Oversight", pp. 12-13). Each variant isolates a different failure mode the active-learning form of semi-supervised RL would need to survive, turning one benchmark into a graded difficulty ladder rather than a single fixed test.