{"arxiv_id":"2311.00252","incoming":{"papers":0,"sentences":0,"groups":[],"unlabelled_sentences":0},"outgoing":{"papers":5,"sentences":9,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":2,"sentences":3,"quotes":[{"arxiv_id":"2004.05155","title":"Learning to Explore using Active Neural SLAM","url":"/paper/2004.05155","section":"Methodology","sentence":"We remark that the Local Planner and the Local Policy do not involve multi-agent interactions, so they are directly adopted from ANS [ref] .=-1","intent_p":1.0,"contests_p":null},{"arxiv_id":"1707.06347","title":"Proximal Policy Optimization Algorithms","url":"/paper/1707.06347","section":"Reinforcement Learning Design","sentence":"We train the by using multi-agent proximal policy optimization (MAPPO) [ref] , which is a multi-agent variant of proximal policy optimization (PPO) [ref] .","intent_p":0.99,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2004.05155","title":"Learning to Explore using Active Neural SLAM","url":"/paper/2004.05155","section":"Spatial Representation","sentence":"Metric maps are grid maps where each grid predicts its traversability [ref] .","intent_p":0.91,"contests_p":null}]}],"unlabelled_sentences":5},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2311.00252"}}