code: def learn(
      network,
      env,
      seed=None,
      nsteps=20,
      total_timesteps=int(80e6),
      q_coef=0.5,
      ent_coef=0.01,
      max_grad_norm=10,
      lr=7e-4,
      lrschedule="linear",
      rprop_epsilon=1e-5,
      rprop_alpha=0.99,
      gamma=0.99,
      log_interval=100,
      buffer_size=50000,
      replay_ratio=4,
      replay_start=10000,
      c=10.0,
      trust_region=True,
      alpha=0.99,
      delta=1,
      load_path=None,
      **network_kwargs,
  ):
      """
      Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
      Train an agent with given network architecture on a given environment using ACER.
  
      Parameters:
      ----------
  
      network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
                          specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
                          tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
                          neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
                          See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
  
      env:                environment. Needs to be vectorized for parallel environment simulation.
                          The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
  
      nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
                          nenv is number of environment copies simulated in parallel) (default: 20)
  
      nstack:             int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
                          (last image dimension) (default: 4)
  
      total_timesteps:    int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
  
      q_coef:             float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
  
      ent_coef:           float, policy entropy coefficient in the optimization objective (default: 0.01)
  
      max_grad_norm:      float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
  
      lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
  
      lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
                          returns fraction of the learning rate (specified as lr) as output
  
      rprop_epsilon:      float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
  
      rprop_alpha:        float, RMSProp decay parameter (default: 0.99)
  
      gamma:              float, reward discounting factor (default: 0.99)
  
      log_interval:       int, number of updates between logging events (default: 100)
  
      buffer_size:        int, size of the replay buffer (default: 50k)
  
      replay_ratio:       int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
  
      replay_start:       int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
  
      c:                  float, importance weight clipping factor (default: 10)
  
      trust_region        bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size  (default: True)
  
      delta:              float, max KL divergence between the old policy and updated policy (default: 1)
  
      alpha:              float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
  
      load_path:          str, path to load the model from (default: None)
  
      **network_kwargs:               keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
                                      For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
  
      """
  
      print("Running Acer Simple")
      print(locals())
      set_global_seeds(seed)
      if not isinstance(env, VecFrameStack):
          env = VecFrameStack(env, 1)
  
      policy = build_policy(
          env, network, estimate_q=True, **network_kwargs
      )
      nenvs = env.num_envs
      ob_space = env.observation_space
      ac_space = env.action_space
  
      nstack = env.nstack
      model = Model(
          policy=policy,
          ob_space=ob_space,
          ac_space=ac_space,
          nenvs=nenvs,
          nsteps=nsteps,
          ent_coef=ent_coef,
          q_coef=q_coef,
          gamma=gamma,
          max_grad_norm=max_grad_norm,
          lr=lr,
          rprop_alpha=rprop_alpha,
          rprop_epsilon=rprop_epsilon,
          total_timesteps=total_timesteps,
          lrschedule=lrschedule,
          c=c,
          trust_region=trust_region,
          alpha=alpha,
          delta=delta,
      )
  
      if load_path is not None:
          model.load(load_path)
  
      runner = Runner(env=env, model=model, nsteps=nsteps)
      if replay_ratio > 0:
          buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
      else:
          buffer = None
      nbatch = nenvs * nsteps
      acer = Acer(runner, model, buffer, log_interval)
      acer.tstart = time.time()
  
      for acer.steps in range(
          0, total_timesteps, nbatch
      ):  # nbatch samples, 1 on_policy call and multiple off-policy calls
          acer.call(on_policy=True)
          if replay_ratio > 0 and buffer.has_atleast(replay_start):
              n = np.random.poisson(replay_ratio)
              for _ in range(n):
                  acer.call(
                      on_policy=False
                  )  # no simulation steps in this
  
      return model
  
imports: import time
  import functools
  import numpy
  import tensorflow
  from baselines import logger
  from baselines.common import set_global_seeds
  from baselines.common.policies import build_policy
  from baselines.common.tf_util import get_session, save_variables, load_variables
  from baselines.common.vec_env.vec_frame_stack import VecFrameStack
  from baselines.a2c.utils import batch_to_seq, seq_to_batch
  from baselines.a2c.utils import cat_entropy_softmax
  from baselines.a2c.utils import Scheduler, find_trainable_variables
  from baselines.a2c.utils import EpisodeStats
  from baselines.a2c.utils import get_by_index, check_shape, avg_norm, gradient_add, q_explained_variance
  from baselines.acer.buffer import Buffer
  from baselines.acer.runner import Runner
label_desc: Use image node_id 3 for calling a global function with example usage: learn(network, env, seed, nsteps, total_timesteps, q_coef, ent_coef, max_grad_norm, lr, lrschedule, rprop_epsilon, rprop_alpha, gamma, log_interval, buffer_size, replay_ratio, replay_start, c, trust_region, alpha, delta, load_path) and returns: model