135 lines
3.9 KiB
Python
Executable File
135 lines
3.9 KiB
Python
Executable File
#!/bin/python3
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import gym
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from gym.envs.registration import register
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import numpy as np
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import os
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import time
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import datetime
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from stable_baselines3 import SAC, PPO, A2C
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, MultiInputActorCriticPolicy
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from sb3_trl.trl_pg import TRL_PG
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import columbus
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#root_path = os.getcwd()
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root_path = '.'
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def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=50000, showRes=False, saveModel=True, n_eval_episodes=16):
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env = gym.make(env_name)
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test_sde = False
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ppo = PPO(
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"MlpPolicy",
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env,
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verbose=0,
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tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo/",
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learning_rate=3e-4,
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gamma=0.99,
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gae_lambda=0.95,
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normalize_advantage=True,
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ent_coef=0.15, # 0.1
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vf_coef=0.5,
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use_sde=False, # False
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)
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trl_pg = TRL_PG(
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"MlpPolicy",
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env,
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verbose=0,
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tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg/",
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learning_rate=3e-4,
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gamma=0.99,
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gae_lambda=0.95,
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normalize_advantage=True,
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ent_coef=0.15, # 0.1
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vf_coef=0.5,
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use_sde=False, # False
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)
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if test_sde:
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ppo_latent_sde = PPO(
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"MlpPolicy",
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env,
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verbose=0,
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tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo_latent_sde/",
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learning_rate=3e-4,
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gamma=0.99,
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gae_lambda=0.95,
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normalize_advantage=True,
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ent_coef=0.15, # 0.1
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vf_coef=0.5,
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use_sde=True, # False
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sde_sample_freq=30*15, # -1
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)
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trl_pg_latent_sde = TRL_PG(
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"MlpPolicy",
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env,
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verbose=0,
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tensorboard_log=root_path+"/logs_tb/"+env_name+"/trl_pg_latent_sde/",
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learning_rate=3e-4,
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gamma=0.99,
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gae_lambda=0.95,
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normalize_advantage=True,
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ent_coef=0.15, # 0.1
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vf_coef=0.5,
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use_sde=True, # False
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sde_sample_freq=30*15, # -1
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)
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# sac_latent_sde = SAC(
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# "MlpPolicy",
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# env,
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# verbose=0,
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# tensorboard_log=root_path+"/logs_tb/"+env_name+"/sac_latent_sde/",
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# use_sde=True,
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# sde_sample_freq=30*15,
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# ent_coef=0.0016, #0.0032
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# gamma=0.99, # 0.95
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# learning_rate=0.001 # 0.015
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# )
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print('TRL_PG:')
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testModel(trl_pg, timesteps, showRes,
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saveModel, n_eval_episodes)
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print('PPO:')
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testModel(ppo, timesteps, showRes,
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saveModel, n_eval_episodes)
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def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):
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env = model.get_env()
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model.learn(timesteps)
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if saveModel:
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now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
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loc = root_path+'/models/' + \
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model.tensorboard_log.replace(
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root_path+'/logs_tb/', '').replace('/', '_')+now+'.zip'
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model.save(loc)
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if n_eval_episodes:
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mean_reward, std_reward = evaluate_policy(
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model, env, n_eval_episodes=n_eval_episodes, deterministic=False)
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print('Reward: '+str(round(mean_reward, 3)) +
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'±'+str(round(std_reward, 2)))
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if showRes:
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input('<ready?>')
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obs = env.reset()
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# Evaluate the agent
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episode_reward = 0
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while True:
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time.sleep(1/30)
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action, _ = model.predict(obs, deterministic=False)
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obs, reward, done, info = env.step(action)
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env.render()
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episode_reward += reward
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if done:
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#print("Reward:", episode_reward)
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episode_reward = 0.0
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obs = env.reset()
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env.reset()
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if __name__ == '__main__':
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main('LunarLanderContinuous-v2')
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