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Recent works present deep reinforcement\nlearning as a framework to model the complex interactions and cooperation.\nHowever, they are implemented using key assumptions about other agents'\nbehavior that deviate from reality as the number of agents in the environment\nincreases. This work extends our previous approach to develop an algorithm that\nlearns collision avoidance among a variety of types of dynamic agents without\nassuming they follow any particular behavior rules. This work also introduces a\nstrategy using LSTM that enables the algorithm to use observations of an\narbitrary number of other agents, instead of previous methods that have a fixed\nobservation size. The proposed algorithm outperforms our previous approach in\nsimulation as the number of agents increases, and the algorithm is demonstrated\non a fully autonomous robotic vehicle traveling at human walking speed, without\nthe use of a 3D Lidar.","url_abs":"http://arxiv.org/abs/1805.01956v1","url_pdf":"http://arxiv.org/pdf/1805.01956v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/mfe7/cadrl_ros","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/miaoruonan/MACA_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/mit-acl/cadrl_ros","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/mit-acl/gym-collision-avoidance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/mit-acl/rl_collision_avoidance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"motion-planning-among-dynamic-decision-making","repo_url":"https://github.com/stevezhang1990/dma_rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collision-avoidance","task_name":"Collision Avoidance"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.01956","atlas_url":"https://app.syntology.ai/?focus=1805.01956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.01956"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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