mirror of
https://github.com/wassname/Deep-reinforcement-learning-with-pytorch.git
synced 2026-09-09 11:13:45 +08:00
53 lines
2.7 KiB
Python
53 lines
2.7 KiB
Python
# coding:utf-8
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import sys
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sys.path.append('../../pythonModules')
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import wg4script, AI_QD, wgdensestranet
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if __name__ == '__main__':
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num_xd, strategy_id_r, strategy_id_b = 0, 0, 0
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num_plays, num_objcutility = 50, 1
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dic2_rolloutaiparas = {
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'red': {'type_ai': AI_QD.AI_QD_BASE,
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'type_stra': 'rule-base',
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'type_stranet': wgdensestranet.StraDenseNet,
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},
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'blue': {'type_ai': AI_QD.AI_QD_STRA,
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'type_stra': 'random',
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'type_stranet': wgdensestranet.StraDenseNet,
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},
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}
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dic_mainparas = {'str_wgrootdir':'../../',
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'str_global_flag': 'QD',
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'num_plays': num_plays,
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'num_objcutility': num_objcutility,
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'num_xd': num_xd,
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'strategy_ids': (strategy_id_r, strategy_id_b),
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'flag_show': True,
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'flag_action_cache': False,
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'flag_qd_rm': True, # flag_qd_rm保存数据库roomrd动作序列
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'flag_cache': False,
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'flag_gpu': False,
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'flag_afm': True, # 两个AI都为BASE时，flag_afm=False; 否则为True
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'flag_dllnum': 0,
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'cuda_id': 0,
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'flag_savestate': False, # flag_savestate： 保存MCTS生成的数据，
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'dic2_aiparas': {
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'flag_color4acai': 0,
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'blue': {'type_ai': AI_QD.AI_QD_HA,
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'type_stra': 'rule-base',
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# type of stratree of nodes, how to select next path, [rule-base, random, net]
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'type_stranet': wgdensestranet.StraDenseNet,
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'dic2_rolloutaiparas': dic2_rolloutaiparas,
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'flag_candidateactions': 'rule-base'
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# [rule-base, stra] how to get candidate actions
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},
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'red': {'type_ai': AI_QD.AI_QD_BASE,
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'type_stra': 'net',
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'type_stranet': wgdensestranet.StraDenseNet,
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'dic2_rolloutaiparas': dic2_rolloutaiparas,
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'flag_candidateactions': 'stra'
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},
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},
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}
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wg4script.simulateframe(dic_mainparas= dic_mainparas) |