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      "statement": "# R2D3 and Hard-Eight: recurrent replay learning from demonstrations\n\nMixed demonstration and self-generated recurrent replay with distributed off-policy Q-learning, and introduced eight sparse-reward partially observed tasks with varied initial conditions.\n\n## Scientific scope\n\nBuilds on R2D2 and demonstration learning. Demo/agent replay ratio is sensitive; solving several Hard-Eight tasks is not all-task exploration closure or deployment safety.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nCaglar Gulcehre; Tom Le Paine; Bobak Shahriari; Misha Denil; Matt Hoffman; Hubert Soyer; Richard Tanburn; Steven Kapturowski; Neil Rabinowitz; Duncan Williams; Gabriel Barth-Maron; Ziyu Wang; Nando de Freitas\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Making Efficient Use of Demonstrations to Solve Hard Exploration Problems](https://arxiv.org/abs/1909.01387v1)\n",
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