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ddpg求解env_part

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along 2025-03-29 12:00:26 +08:00
parent 0cf336c96d
commit f05f8400fb
2 changed files with 10 additions and 9 deletions
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10
DDPG_solver/main.py
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@ -9,7 +9,7 @@ import torch
import sys import sys
import os import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from env import PartitionMazeEnv from env_partion import PartitionEnv
# fmt: on # fmt: on
'''Hyperparameter Setting''' '''Hyperparameter Setting'''
@ -54,16 +54,16 @@ print(opt)
def main(): def main():
EnvName = ['PartitionMaze_DDPG', 'Pendulum-v1', 'LunarLanderContinuous-v2', 'Humanoid-v4', EnvName = ['Partition_DDPG', 'Pendulum-v1', 'LunarLanderContinuous-v2', 'Humanoid-v4',
'HalfCheetah-v4', 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3'] 'HalfCheetah-v4', 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3']
BrifEnvName = ['PM_DDPG', 'PV1', 'LLdV2', BrifEnvName = ['Part_DDPG', 'PV1', 'LLdV2',
'Humanv4', 'HCv4', 'BWv3', 'BWHv3'] 'Humanv4', 'HCv4', 'BWv3', 'BWHv3']
# Build Env # Build Env
# env = gym.make(EnvName[opt.EnvIdex], render_mode = "human" if opt.render else None) # env = gym.make(EnvName[opt.EnvIdex], render_mode = "human" if opt.render else None)
env = PartitionMazeEnv() env = PartitionEnv()
# eval_env = gym.make(EnvName[opt.EnvIdex]) # eval_env = gym.make(EnvName[opt.EnvIdex])
eval_env = PartitionMazeEnv() eval_env = PartitionEnv()
opt.state_dim = env.observation_space.shape[0] opt.state_dim = env.observation_space.shape[0]
opt.action_dim = env.action_space.shape[0] opt.action_dim = env.action_space.shape[0]
# remark: action space【-max,max】 # remark: action space【-max,max】

9
env_partion.py
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@ -18,11 +18,11 @@ class PartitionEnv(gym.Env):
############################## ##############################
# 可能需要手动修改的超参数 # 可能需要手动修改的超参数
############################## ##############################
self.params = 'params3' self.params = 'params2'
self.CUT_NUM = 2 self.CUT_NUM = 4
self.ROW_CUT_LIMIT = 1 self.ROW_CUT_LIMIT = 3
self.COL_CUT_LIMIT = 1 self.COL_CUT_LIMIT = 1
self.BASE_LINE = 5000 self.BASE_LINE = 10000
self.mTSP_STEPS = 10000 self.mTSP_STEPS = 10000
# 车队参数设置 # 车队参数设置
@ -176,6 +176,7 @@ class PartitionEnv(gym.Env):
# print(best_path) # print(best_path)
reward += self.BASE_LINE - best_time reward += self.BASE_LINE - best_time
print(reward)
return state, reward, True, False, best_path return state, reward, True, False, best_path