testing_model.py

import gym
import ArmEnv
import matplotlib.pyplot as plt
import numpy as np
import pickle

from stable_baselines3.common.monitor import Monitor
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import CheckpointCallback
from stable_baselines3 import SAC



env = gym.make('PointToPoint-v0',gui=True,mode='P',record=True)

model = PPO.load("logs/best_model_75.zip", env=env,custom_objects={"learning_rate": 0.0,
            "lr_schedule": lambda _: 0.0,
            "clip_range": lambda _: 0.0,})
#model = PPO.load("trial.zip", env=env)

rew = []

for i in range(1):
    done = False
    obs = env.reset()
    while not done:
        action, _state =model.predict(obs)
        #action = np.array([0,0,0,0,0,0,0])
        obs,reward,done,_ = env.step(action)
        print(reward)
        print(obs)
        print(action)
        #print(action)
        if i==0:
            rew.append(reward)

t = np.arange(len(rew))
print(sum(rew))

fig,ax = plt.subplots()
ax.plot(t,rew)
ax.set_title("Testing")
ax.set_xlabel("Timesteps")
ax.set_ylabel("Rewards")
plt.show()
#goal: [0.4,0,1.1]  loc6:  [0.44738302 0.04233105 1.11781087]
#goal: [0.6,0,0.9]  loc6:  [0.5669553  0.07910315 1.04583075]
#goal: [0.3,0,1.2]  loc6:  [0.27454447 0.03467133 1.20632827]