{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantum-deep-reinforcement-learning-for-robot","title":"Quantum Deep Reinforcement Learning for Robot Navigation Tasks","arxiv_id":"2202.12180","date":"2022-02-24","proceeding":null,"authors":["Hans Hohenfeld","Dirk Heimann","Felix Wiebe","Frank Kirchner"],"abstract":"We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train parameterized quantum circuits (PQCs) with two different encoding strategies in a hybrid quantum-classical setup as well as a classical neural network baseline with the double deep Q network (DDQN) reinforcement learning algorithm. Quantum deep reinforcement learning (QDRL) has previously been studied in several relatively simple benchmark environments, mainly from the OpenAI gym suite. However, scaling behavior and applicability of QDRL to more demanding tasks closer to real-world problems e. g., from the robotics domain, have not been studied previously. Here, we show that quantum circuits in hybrid quantum-classic reinforcement learning setups are capable of learning optimal policies in multiple robotic navigation scenarios with notably fewer trainable parameters compared to a classical baseline. Across a large number of experimental configurations, we find that the employed quantum circuits outperform the classical neural network baselines when equating for the number of trainable parameters. Yet, the classical neural network consistently showed better results concerning training times and stability, with at least one order of magnitude of trainable parameters more than the best-performing quantum circuits. However, validating the robustness of the learning methods in a large and dynamic environment, we find that the classical baseline produces more stable and better performing policies overall.","url_abs":"https://arxiv.org/abs/2202.12180v3","url_pdf":"https://arxiv.org/pdf/2202.12180v3.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":"quantum-deep-reinforcement-learning-for-robot","repo_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"quantum-deep-reinforcement-learning-for-robot","repo_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":"quantum-machine-learning","task_name":"Quantum Machine Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2202.12180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.12180"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"unverified":9},"by_repo_kind":{"official":{"samples":9,"ran":0,"repositories":2}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"622209d3d7626703","entry":"compute_avg_reward","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/runner.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/runner.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"622209d3d7626703"}},{"code_sha256_prefix":"8c3cf11a69877944","entry":"default_builder","repo":"dfki-ric-quantum/qdrl-turtlebot-env","repo_kind":"official","path":"qturtle/builders/default.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env/blob/HEAD/qturtle/builders/default.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"8c3cf11a69877944"}},{"code_sha256_prefix":"7c816ef705de7628","entry":"get_new_param","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/quantum_circuit.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/quantum_circuit.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"7c816ef705de7628"}},{"code_sha256_prefix":"9ac0dddb3211bc21","entry":"lidar_env_builder","repo":"dfki-ric-quantum/qdrl-turtlebot-env","repo_kind":"official","path":"qturtle/builders/default.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env/blob/HEAD/qturtle/builders/default.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"9ac0dddb3211bc21"}},{"code_sha256_prefix":"c54986e5c79ff74d","entry":"make","repo":"dfki-ric-quantum/qdrl-turtlebot-env","repo_kind":"official","path":"qturtle/core.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env/blob/HEAD/qturtle/core.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c54986e5c79ff74d"}},{"code_sha256_prefix":"3a4f8ceb50bb75ab","entry":"process_reward","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/frozenlake.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/frozenlake.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3a4f8ceb50bb75ab"}},{"code_sha256_prefix":"20234be6a42360b2","entry":"save_results","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/utils.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"20234be6a42360b2"}},{"code_sha256_prefix":"3402dfd16d7cdaca","entry":"to_binary","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/frozenlake.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/frozenlake.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3402dfd16d7cdaca"}},{"code_sha256_prefix":"5819d9fdcbfeb812","entry":"to_tuple","repo":"dfki-ric-quantum/qdrl-turtlebot-eval","repo_kind":"official","path":"qeval/quantum_circuit.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval/blob/HEAD/qeval/quantum_circuit.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"5819d9fdcbfeb812"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}