def test_episode_count(self):
cartpole_env = gym.make('CartPole-v1')
env = gym_wrapper.GymWrapper(cartpole_env)
env = wrappers.RunStats(env)
self.assertEqual(0, env.episodes)
time_step = env.reset()
self.assertEqual(0, env.episodes)
for episode_num in range(1, 4):
while not time_step.is_last():
time_step = env.step(np.array(1, dtype=np.int32))
self.assertEqual(episode_num, env.episodes)
time_step = env.step(np.array(1, dtype=np.int32))
def test_episode_count_with_time_limit(self):
cartpole_env = gym.make('CartPole-v1')
env = gym_wrapper.GymWrapper(cartpole_env)
env = wrappers.TimeLimit(env, 2)
env = wrappers.RunStats(env)
env.reset()
self.assertEqual(0, env.episodes)
env.step(np.array(0, dtype=np.int32))
time_step = env.step(np.array(0, dtype=np.int32))
self.assertTrue(time_step.is_last())
self.assertEqual(1, env.episodes)
def test_resets_count(self):
cartpole_env = gym.make('CartPole-v1')
env = gym_wrapper.GymWrapper(cartpole_env)
env = wrappers.RunStats(env)
self.assertEqual(0, env.resets)
time_step = env.reset()
self.assertEqual(1, env.resets)
resets = 1
for _ in range(0, 4):
while not time_step.is_last():
self.assertEqual(resets, env.resets)
time_step = env.step(np.array(1, dtype=np.int32))
time_step = env.step(np.array(1, dtype=np.int32))
resets += 1
def test_step_count(self):
cartpole_env = gym.make('CartPole-v1')
env = gym_wrapper.GymWrapper(cartpole_env)
env = wrappers.RunStats(env)
self.assertEqual(0, env.episodes)
time_step = env.reset()
self.assertEqual(0, env.episodes)
steps = 0
for _ in range(0, 4):
while not time_step.is_last():
self.assertEqual(steps, env.total_steps)
time_step = env.step(np.array(1, dtype=np.int32))
steps += 1
time_step = env.step(np.array(1, dtype=np.int32))
from dual_goal_maze_env import DualGoalMaze
from tf_agents.environments import utils
from tf_agents.environments import wrappers
import tensorflow as tf
import numpy as np
from tf_agents.environments import py_environment
from tf_agents.environments import tf_environment
from tf_agents.environments import tf_py_environment
from tf_agents.specs import array_spec
from tf_agents.environments import suite_gym
from tf_agents.trajectories import time_step as ts
environment = DualGoalMaze()
stats_env = wrappers.RunStats(environment)
utils.validate_py_environment(stats_env, episodes=5)
time_step = stats_env.reset()
rewards = []
steps = []
num_episodes = 5
for _ in range(num_episodes):
episode_reward = 0
episode_steps = 0
while not time_step.is_last():
action = np.random.randint(0, 4)
time_step = stats_env.step(action)
episode_steps += 1
episode_reward += time_step.reward
