96 lines
1.9 KiB
Python
96 lines
1.9 KiB
Python
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import sys
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import gym
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from flask import Flask
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from flask import request
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import pickle
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import json
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##
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# OpenAI Gym State
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##
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# environment_name = sys.argv[1]
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# environment_name = "Acrobot-v1"
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environment_name = "Pong-v0"
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env = gym.make(environment_name)
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# Observations to release to agent
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state = env.reset()
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reward = 0
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score = 0
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done = False
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info = {}
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##
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# Helper Functions
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##
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# [TODO] Evaluate whether pickling is the right option here
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def pickle_state():
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global state
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return pickle.dumps(state)
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##
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# Flask Environment
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##
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app = Flask(__name__)
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@app.route('/environment', methods=['GET'])
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def get_env():
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global env, environment_name
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if request.args.get('shape') is not None:
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shape = {}
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shape['observation'] = env.observation_space.shape
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shape['action'] = env.action_space.n
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return json.dumps(shape)
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return environment_name
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@app.route('/state', methods=['GET'])
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def get_state():
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return pickle_state()
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@app.route('/reward', methods=['GET'])
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def get_reward():
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global score, reward
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if request.args.get('all') is not None:
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return str(score)
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else:
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return str(reward)
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@app.route('/done', methods=['GET'])
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def is_done():
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global done
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return str(done)
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@app.route('/info', methods=['GET'])
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def get_info():
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global info
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return json.dumps(info)
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@app.route('/action', methods=['POST'])
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def perform_action():
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global state, reward, done, info, score
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action = int(request.form['id'])
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# [TODO] Check to see if 'action' is valid
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state, reward, done, info = env.step(action)
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score += reward
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content = {}
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content['reward'] = reward
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content['done'] = done
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content['info'] = info
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return json.dumps(content)
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@app.route('/reset')
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def reset_env():
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global env, state, reward, done, info, score
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state = env.reset()
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reward = 0
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done = False
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info = {}
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score = 0
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return pickle_state()
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