Adriel-M

 YN0000: ┌ Fetch step
➤ YN0066: │ typescript@patch:typescript@npm%3A5.3.2#~builtin<compat/typescript>::version=5.3.2&hash=14eedb: Cannot apply hunk #2
➤ YN0013: │ 56 packages were already cached, 1707 had to be fetched
➤ YN0000: └ Completed in 2m 52s
➤ YN0000: ┌ Link step
➤ YN0007: │ esbuild@npm:0.18.20 must be built because it never has been before or the last one failed
➤ YN0007: │ esbuild@npm:0.18.11 must be built because it never has been before or the last one failed
➤ YN0007: │ yarn@npm:1.22.21 must be built because it never has been before or the last one failed
➤ YN0007: │ better-sqlite3@npm:8.7.0 must be built because it never has been before or the last one failed
➤ YN0007: │ protobufjs@npm:7.2.5 must be built because it never has been before or the last one failed

Adriel-M

layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "label"
  python_param {
    module: "layers"
    layer: "InputLayer"
    param_str: "{\'seed\': 1337, \'split\': \'train\', \'work_dir\': \'../work\'}"
  }

Adriel-M

# REINFORCE: Monte Carlo Policy Gradient Implementation
# Learn more from Reinforcement Learning: An Introduction (p271)
# by Sutton & Barto

import tensorflow as tf
import gym
import numpy as np
from gym import wrappers

Adriel-M

# REINFORCE: Monte Carlo Policy Gradient Implementation
# Learn more from Reinforcement Learning: An Introduction (p271)
# by Sutton & Barto

import tensorflow as tf
import gym
import numpy as np
from gym import wrappers

Adriel-M

# REINFORCE: Monte Carlo Policy Gradient Implementation
# Learn more from Reinforcement Learning: An Introduction (p271)
# by Sutton & Barto

import tensorflow as tf
import gym
import numpy as np
from gym import wrappers

Adriel-M

# REINFORCE: Monte Carlo Policy Gradient Implementation
# Learn more from Reinforcement Learning: An Introduction (p271)
# by Sutton & Barto

import tensorflow as tf
import gym
import numpy as np
from gym import wrappers

Adriel-M

# Evolutionary Learning Strategy Implementation
# Learn more from https://blog.openai.com/evolution-strategies/

import gym
import numpy as np
from gym import wrappers

# GLOBAL SETTINGS
RNG_SEED = 8
# ENVIRONMENT = "CartPole-v0"

Adriel-M

# Evolutionary Learning Strategy Implementation
# Learn more from https://blog.openai.com/evolution-strategies/

import gym
import numpy as np
from gym import wrappers

# GLOBAL SETTINGS
RNG_SEED = 8
ENVIRONMENT = "CartPole-v0"

Adriel-M

# Evolutionary Learning Strategy Implementation
# Learn more from https://blog.openai.com/evolution-strategies/

import gym
import numpy as np
from gym import wrappers

# GLOBAL SETTINGS
RNG_SEED = 8
POPULATION_SIZE = 100  # Population size

Adriel-M

# Evolutionary Learning Strategy Implementation
# Learn more from https://blog.openai.com/evolution-strategies/

import gym
import numpy as np
from gym import wrappers

# GLOBAL SETTINGS
RNG_SEED = 8
POPULATION_SIZE = 100  # Population size