feature(xcy): add muzero config for connect4

zoo/board_games/connect4/config/connect4_muzero_bot_mode_config.py

@@ -0,0 +1,83 @@
+from easydict import EasyDict
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+collector_env_num = 8
+n_episode = 8
+evaluator_env_num = 5
+num_simulations = 50
+update_per_collect = 50
+reanalyze_ratio = 0.
+batch_size = 256
+max_env_step = int(5e5)
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+
+connect4_muzero_config = dict(
+    exp_name=
+    f'data_mz_ctree/connect4_botmode_rulebot_seed0',
+    env=dict(
+        battle_mode='play_with_bot_mode',
+        bot_action_type='rule',
+        channel_last=True,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+    ),
+    policy=dict(
+        model=dict(
+            observation_shape=(3, 6, 7),
+            action_space_size=7,
+            image_channel=3,
+            num_res_blocks=1,
+            num_channels=64,
+            support_scale=300,
+            reward_support_size=601,
+            value_support_size=601,
+        ),
+        cuda=True,
+        env_type='board_games',
+        game_segment_length=int(6 * 7 / 2),  # for battle_mode='play_with_bot_mode'
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='Adam',
+        lr_piecewise_constant_decay=False,
+        learning_rate=0.003,
+        grad_clip_value=0.5,
+        num_simulations=num_simulations,
+        reanalyze_ratio=reanalyze_ratio,
+        # NOTE：In board_games, we set large td_steps to make sure the value target is the final outcome.
+        td_steps=int(6 * 7 / 2),  # for battle_mode='play_with_bot_mode'
+        # NOTE：In board_games, we set discount_factor=1.
+        discount_factor=1,
+        n_episode=n_episode,
+        eval_freq=int(2e3),
+        replay_buffer_size=int(1e5),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+connect4_muzero_config = EasyDict(connect4_muzero_config)
+main_config = connect4_muzero_config
+
+connect4_muzero_create_config = dict(
+    env=dict(
+        type='connect4',
+        import_names=['zoo.board_games.connect4.envs.connect4_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='muzero',
+        import_names=['lzero.policy.muzero'],
+    ),
+)
+connect4_muzero_create_config = EasyDict(connect4_muzero_create_config)
+create_config = connect4_muzero_create_config
+
+if __name__ == "__main__":
+    from lzero.entry import train_muzero
+
+    train_muzero([main_config, create_config], seed=1, max_env_step=max_env_step)

zoo/board_games/connect4/config/connect4_muzero_sp_mode_config.py

@@ -0,0 +1,83 @@
+from easydict import EasyDict
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+collector_env_num = 8
+n_episode = 8
+evaluator_env_num = 5
+num_simulations = 50
+update_per_collect = 50
+reanalyze_ratio = 0.
+batch_size = 256
+max_env_step = int(5e5)
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+
+connect4_muzero_config = dict(
+    exp_name=
+    f'data_mz_ctree/connect4_spmode_rulebot_seed0',
+    env=dict(
+        battle_mode='self_play_mode',
+        bot_action_type='rule',
+        channel_last=True,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+    ),
+    policy=dict(
+        model=dict(
+            observation_shape=(3, 6, 7),
+            action_space_size=7,
+            image_channel=3,
+            num_res_blocks=1,
+            num_channels=64,
+            support_scale=300,
+            reward_support_size=601,
+            value_support_size=601,
+        ),
+        cuda=True,
+        env_type='board_games',
+        game_segment_length=int(6 * 7),  # for battle_mode='self_play_mode'
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='Adam',
+        lr_piecewise_constant_decay=False,
+        learning_rate=0.003,
+        grad_clip_value=0.5,
+        num_simulations=num_simulations,
+        reanalyze_ratio=reanalyze_ratio,
+        # NOTE：In board_games, we set large td_steps to make sure the value target is the final outcome.
+        td_steps=int(6 * 7),  # for battle_mode='self_play_mode'
+        # NOTE：In board_games, we set discount_factor=1.
+        discount_factor=1,
+        n_episode=n_episode,
+        eval_freq=int(2e3),
+        replay_buffer_size=int(1e5),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+connect4_muzero_config = EasyDict(connect4_muzero_config)
+main_config = connect4_muzero_config
+
+connect4_muzero_create_config = dict(
+    env=dict(
+        type='connect4',
+        import_names=['zoo.board_games.connect4.envs.connect4_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='muzero',
+        import_names=['lzero.policy.muzero'],
+    ),
+)
+connect4_muzero_create_config = EasyDict(connect4_muzero_create_config)
+create_config = connect4_muzero_create_config
+
+if __name__ == "__main__":
+    from lzero.entry import train_muzero
+
+    train_muzero([main_config, create_config], seed=1, max_env_step=max_env_step)