metastable-baselines/test.py

#!/usr/bin/python3
import gym
import time
import datetime

from stable_baselines3.common.evaluation import evaluate_policy
from metastable_baselines.distributions.distributions import get_legal_setups

from metastable_baselines.ppo import PPO
from metastable_baselines.sac import SAC
from metastable_baselines.ppo.policies import MlpPolicy as MlpPolicyPPO
from metastable_baselines.sac.policies import MlpPolicy as MlpPolicySAC
from metastable_projections.projections import BaseProjectionLayer, FrobeniusProjectionLayer, WassersteinProjectionLayer, KLProjectionLayer
import columbus

from metastable_baselines.distributions import Strength, ParametrizationType, EnforcePositiveType, ProbSquashingType

import torch as th

root_path = '.'


def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=1_000_000, showRes=True, saveModel=True, n_eval_episodes=0):
    env = gym.make(env_name)
    use_sde = True
    # th.autograd.set_detect_anomaly(True)
    #sac = SAC(
    #    MlpPolicySAC,
    #    env,
        # KLProjectionLayer(trust_region_coeff=0.01),
        #projection=WassersteinProjectionLayer(trust_region_coeff=0.01),
    #    policy_kwargs={'dist_kwargs': {'neural_strength': Strength.NONE, 'cov_strength': Strength.DIAG, 'parameterization_type':
    #                   ParametrizationType.NONE, 'enforce_positive_type': EnforcePositiveType.ABS, 'prob_squashing_type': ProbSquashingType.NONE}},
    #    verbose=0,
    #    tensorboard_log=root_path+"/logs_tb/" +
    #    env_name+"/sac"+(['', '_sde'][use_sde])+"/",
    #    learning_rate=3e-4,  # 3e-4,
    #    gamma=0.99,
        #gae_lambda=0.95,
        #normalize_advantage=True,
        #ent_coef=0.1,  # 0.1
        #vf_coef=0.5,
    #    use_sde=use_sde,  # False
    #    sde_sample_freq=8,
        #clip_range=None  # 1  # 0.2,
    #)
    trl_frob = PPO(
       MlpPolicyPPO,
       env,
       projection=FrobeniusProjectionLayer(),
       verbose=0,
       tensorboard_log=root_path+"/logs_tb/"+env_name +
       "/trl_frob"+(['', '_sde'][use_sde])+"/",
       learning_rate=3e-4,
       gamma=0.99,
       gae_lambda=0.95,
       normalize_advantage=True,
       ent_coef=0.03,  # 0.1
       vf_coef=0.5,
       use_sde=use_sde,
       clip_range=2,  # 0.2
    )

    #print('SAC:')
    #testModel(sac, timesteps, showRes,
    #          saveModel, n_eval_episodes)
    print('TRL_frob:')
    testModel(trl_frob, timesteps, showRes,
             saveModel, n_eval_episodes)


def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, saveModel=True, n_eval_episodes=4):
    env = gym.make(env_name)
    use_sde = False
    skip_num = 4  # 10 (/ start at index)
    sac = False
    Model = [PPO, SAC][sac]
    Policy = [MlpPolicyPPO, MlpPolicySAC][sac]
    projection = FrobeniusProjectionLayer()
    #projection = BaseProjectionLayer()

    gen = enumerate(get_legal_setups(
        allowedEPTs=[EnforcePositiveType.SOFTPLUS, EnforcePositiveType.ABS]))
    for i in range(skip_num):
        gen.__next__()
    for i, setup in gen:
        (ps, cs, ept, pt) = setup
        print('{'+str(i)+'}: '+str(setup))
        model = Model(
            Policy,
            env,
            projection=projection,
            policy_kwargs={'dist_kwargs': {'neural_strength': ps, 'cov_strength': cs, 'parameterization_type':
                                           pt, 'enforce_positive_type': ept, 'prob_squashing_type': ProbSquashingType.TANH}},
            verbose=0,
            tensorboard_log=root_path+"/logs_tb/" +
            env_name+"/"+['ppo', 'sac'][sac]+"_" + 'TANH_' +
            ("_".join([str(s) for s in setup])+['', '_sde'][use_sde])+"/",
            learning_rate=3e-4,
            gamma=0.99,
            gae_lambda=0.95,
            normalize_advantage=True,
            ent_coef=0.02,  # 0.1
            vf_coef=0.5,
            use_sde=use_sde,  # False
            clip_range=1  # 0.2,
        )

        testModel(model, timesteps, False,
                  saveModel, n_eval_episodes)


def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):
    env = model.get_env()
    try:
        model.learn(timesteps)
    except KeyboardInterrupt:
        print('[!] Training Terminated')
        pass

    if saveModel:
        now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
        loc = root_path+'/models/' + \
            model.tensorboard_log.replace(
                root_path+'/logs_tb/', '').replace('/', '_')+now+'.zip'
        model.save(loc)

    if n_eval_episodes:
        mean_reward, std_reward = evaluate_policy(
            model, env, n_eval_episodes=n_eval_episodes, deterministic=False)
        print('Reward: '+str(round(mean_reward, 3)) +
              '±'+str(round(std_reward, 2)))

    if showRes:
        input('<ready?>')
        obs = env.reset()
        # Evaluate the agent
        episode_reward = 0
        while True:
            time.sleep(1/30)
            action, _ = model.predict(obs, deterministic=False)
            obs, reward, done, info = env.step(action)
            env.render()
            episode_reward += reward
            if done:
                # print("Reward:", episode_reward)
                episode_reward = 0.0
                obs = env.reset()
    env.reset()


if __name__ == '__main__':
    main('LunarLanderContinuous-v2')
    # main('ColumbusJustState-v0')
    # main('ColumbusStateWithBarriers-v0')
    # full('ColumbusEasierObstacles-v0')
    # main('ColumbusSingle-v0')
    # full('LunarLanderContinuous-v2')