:og:description: Ordering-based Reinforcement Learning :og:image:alt: Benchpress logo :og:sitename: Benchpress causal discovery platform :og:title: CORL (gCastle) .. meta:: :title: CORL (gCastle) :description: Ordering-based Reinforcement Learning .. _gcastle_corl: CORL (gCastle) *************** .. list-table:: * - Module name - `gcastle_corl `__ * - Package - `gCastle `__ * - Version - 1.0.3 * - Language - `Python `__ * - Docs - `here `__ * - Paper - :footcite:t:`wang2021ordering` * - Graph type - `DAG `__ * - MCMC - No * - Edge constraints - No * - Data type - C * - Data missingness - * - Intervention type - * - Docker - `bpimages/gcastle:1.0.3 `__ Ordering-based Reinforcement Learning ----------------------------------------- A RL- and order-based algorithm that improves the efficiency and scalability of previous RL-based approach. .. rubric:: Example JSON .. code-block:: json [ { "id": "gcastle_corl", "batch_size": 64, "input_dim": 100, "embed_dim": 256, "normalize": false, "encoder_name": "transformer", "encoder_heads": 8, "encoder_blocks": 3, "encoder_dropout_rate": 0.1, "decoder_name": "lstm", "reward_mode": "episodic", "reward_score_type": "BIC", "reward_regression_type": "LR", "reward_gpr_alpha": 1.0, "iteration": 10, "actor_lr": "1e-4", "critic_lr": "1e-3", "alpha": 0.99, "init_baseline": -1.0, "random_seed": 0, "device_type": "cpu", "device_ids": 0, "timeout": null } ] .. footbibliography::