{"url":"/task/control-with-prametrised-actions","name":"Control with Prametrised Actions","slug":"control-with-prametrised-actions","description_markdown":"Most reinforcement learning research papers focus on environments where the agent’s actions are either discrete or continuous. However, when training an agent to play a video game, it is common to encounter situations where actions have both discrete and continuous components. For example,  a set of high-level discrete actions (ex: move, jump, fire), each of them being associated with continuous parameters (ex: target coordinates for the move action, direction for the jump action, aiming angle for the fire action). These kinds of tasks are included in Control with Parameterised Actions.","categories":[{"name":"Playing Games","url":"/area/playing-games"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":2,"papers_with_code":2,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/control-with-prametrised-actions-on-half","slug":"control-with-prametrised-actions-on-half","dataset":"Half Field Offence","dataset_url":null,"rows_in_archive":2,"metrics":["Goal Probability"],"first_row_in_archive_order":{"model":"MP-DQN","paper_title":"Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces","paper_url":"/paper/multi-pass-q-networks-for-deep-reinforcement","paper_date":"2019-05-10","arxiv_id":"1905.04388","code_links":[{"title":"opendilab/DI-engine","url":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py"},{"title":"cycraig/MP-DQN","url":"https://github.com/cycraig/MP-DQN"},{"title":"cycraig/gym-platform","url":"https://github.com/cycraig/gym-platform"},{"title":"cycraig/gym-goal","url":"https://github.com/cycraig/gym-goal"}],"syntology":null}},{"leaderboard":"/sota/control-with-prametrised-actions-on-platform","slug":"control-with-prametrised-actions-on-platform","dataset":"Platform","dataset_url":null,"rows_in_archive":2,"metrics":["Return"],"first_row_in_archive_order":{"model":"MP-DQN","paper_title":"Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces","paper_url":"/paper/multi-pass-q-networks-for-deep-reinforcement","paper_date":"2019-05-10","arxiv_id":"1905.04388","code_links":[{"title":"opendilab/DI-engine","url":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py"},{"title":"cycraig/MP-DQN","url":"https://github.com/cycraig/MP-DQN"},{"title":"cycraig/gym-platform","url":"https://github.com/cycraig/gym-platform"},{"title":"cycraig/gym-goal","url":"https://github.com/cycraig/gym-goal"}],"syntology":null}},{"leaderboard":"/sota/control-with-prametrised-actions-on-robot","slug":"control-with-prametrised-actions-on-robot","dataset":"Robot Soccer Goal","dataset_url":null,"rows_in_archive":2,"metrics":["Goal Probability"],"first_row_in_archive_order":{"model":"MP-DQN","paper_title":"Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces","paper_url":"/paper/multi-pass-q-networks-for-deep-reinforcement","paper_date":"2019-05-10","arxiv_id":"1905.04388","code_links":[{"title":"opendilab/DI-engine","url":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py"},{"title":"cycraig/MP-DQN","url":"https://github.com/cycraig/MP-DQN"},{"title":"cycraig/gym-platform","url":"https://github.com/cycraig/gym-platform"},{"title":"cycraig/gym-goal","url":"https://github.com/cycraig/gym-goal"}],"syntology":null}}],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":2,"of":2,"tagged_in_all":2,"items":[{"url":"/paper/multi-pass-q-networks-for-deep-reinforcement","title":"Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces","date":"2019-05-10","arxiv_id":"1905.04388","repositories_listed":4,"syntology":null},{"url":"/paper/discrete-and-continuous-action-representation","title":"Discrete and Continuous Action Representation for Practical RL in Video Games","date":"2019-12-23","arxiv_id":"1912.11077","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}