Koha9
763d704efd
add save button@Unity then send "saveNow" Toggle to Python. delete useless method "saveModel" and "loadModel". Use Save/load weights instead.
98 lines
4.4 KiB
Python
98 lines
4.4 KiB
Python
import mlagents_envs
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from mlagents_envs.base_env import ActionTuple
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from mlagents_envs.environment import UnityEnvironment
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import numpy as np
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class makeEnv(object):
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def __init__(self,envPath,workerID,basePort):
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self.env = UnityEnvironment(file_name=envPath,seed = 1,side_channels=[],worker_id = workerID,base_port=basePort)
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self.env.reset()
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# get enviroment specs
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self.LOAD_DIR_SIZE_IN_STATE = 3
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self.TRACKED_AGENT = -1
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self.BEHA_SPECS = self.env.behavior_specs
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self.BEHA_NAME = list(self.BEHA_SPECS)[0]
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self.SPEC = self.BEHA_SPECS[self.BEHA_NAME]
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self.OBSERVATION_SPECS = self.SPEC.observation_specs[0] # observation spec
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self.ACTION_SPEC = self.SPEC.action_spec # action specs
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self.DISCRETE_SIZE = self.ACTION_SPEC.discrete_size# 連続的な動作のSize
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self.CONTINUOUS_SIZE = self.ACTION_SPEC.continuous_size# 離散的な動作のSize
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self.STATE_SIZE = self.OBSERVATION_SPECS.shape[0] - self.LOAD_DIR_SIZE_IN_STATE# 環境観測データ数
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print("√√√√√Enviroment Initialized Success√√√√√")
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def step(self,discreteActions = None,continuousActions = None,behaviorName = None,trackedAgent = None):
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# take action to enviroment
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# return mextState,reward,done
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# check if arg is include None or IS None
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try:
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isDisNone = discreteActions.any() == None
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if discreteActions.all() == None:
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print("step() Error!:discreteActions include None")
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except:
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isDisNone = True
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try:
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isConNone = continuousActions.any() == None
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if continuousActions.all() == None:
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print("step() Error!:continuousActions include None")
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except:
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isConNone = True
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if isDisNone:
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# if discreteActions is enpty just give nothing[[0]] to Enviroment
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discreteActions = np.array([[0]], dtype=np.int)
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if isConNone:
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# if continuousActions is enpty just give nothing[[0]] to Enviroment
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continuousActions = np.array([[0]], dtype=np.float)
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if behaviorName == None:
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behaviorName = self.BEHA_NAME
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if trackedAgent == None:
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trackedAgent = self.TRACKED_AGENT
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#create actionTuple
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thisActionTuple = ActionTuple(continuous=continuousActions,discrete=discreteActions)
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# take action to env
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self.env.set_actions(behavior_name=behaviorName,action=thisActionTuple)
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self.env.step()
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# get nextState & reward & done after this action
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nextState,reward,done,loadDir, saveNow = self.getSteps(behaviorName,trackedAgent)
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return nextState,reward,done,loadDir, saveNow
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def getSteps(self,behaviorName = None,trackedAgent = None):
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# get nextState & reward & done
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if behaviorName == None:
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behaviorName = self.BEHA_NAME
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decisionSteps,terminalSteps = self.env.get_steps(behaviorName)
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if self.TRACKED_AGENT == -1 and len(decisionSteps) >= 1:
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self.TRACKED_AGENT = decisionSteps.agent_id[0]
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if trackedAgent == None:
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trackedAgent = self.TRACKED_AGENT
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if trackedAgent in decisionSteps: # ゲーム終了していない場合、環境状態がdecision_stepsに保存される
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nextState = decisionSteps[trackedAgent].obs[0]
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nextState = np.reshape(nextState,[1,self.STATE_SIZE+self.LOAD_DIR_SIZE_IN_STATE])
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saveNow = nextState[0][-1]
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loadDir = nextState[0][-3:-1]
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nextState = nextState[0][:-3]
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reward = decisionSteps[trackedAgent].reward
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done = False
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if trackedAgent in terminalSteps: # ゲーム終了した場合、環境状態がterminal_stepsに保存される
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nextState = terminalSteps[trackedAgent].obs[0]
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nextState = np.reshape(nextState,[1,self.STATE_SIZE+self.LOAD_DIR_SIZE_IN_STATE])
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saveNow = nextState[0][-1]
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loadDir = nextState[0][-3:-1]
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nextState = nextState[0][:-3]
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reward = terminalSteps[trackedAgent].reward
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done = True
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return nextState, reward, done, loadDir, saveNow
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def reset(self):
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self.env.reset()
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nextState,reward,done,loadDir,saveNow = self.getSteps()
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return nextState,reward,done,loadDir,saveNow
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def render(self):
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self.env.render() |