step_plot = []
num_iterations = 20000 # @param {type:"integer"}
initial_collect_steps = 100 # @param {type:"integer"}
collect_steps_per_iteration = 1 # @param {type:"integer"}
replay_buffer_max_length = 100000 # @param {type:"integer"}
batch_size = 64 # @param {type:"integer"}
learning_rate = 1e-3 # @param {type:"number"}
log_interval = 200 # @param {type:"integer"}
num_eval_episodes = 10 # @param {type:"integer"}
eval_interval = 1000 # @param {type:"integer"}
env = Environment.CnfSolverEnv()
train_py_env = Environment.CnfSolverEnv()
eval_py_env = Environment.CnfSolverEnv()
train_env = tf_py_environment.TFPyEnvironment(train_py_env)
eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)
fc_layer_params = (100, 50)
action_tensor_spec = tensor_spec.from_spec(env.action_spec())
num_actions = action_tensor_spec.maximum - action_tensor_spec.minimum + 1
# Define a helper function to create Dense layers configured with the right
# activation and kernel initializer.
def dense_layer(num_units):
import numpy as np
import Environment as env
import random
import time
cnfEnv = env.CnfSolverEnv()
action_array = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 99]
cnfEnv.reset()
for _ in range(100):
action = random.choice(action_array)
cnfEnv.step(action)
time.sleep(0.5)
