Model = ActorCritic(num_actions, num_hidden_units) mon = layers.Dense(num_hidden_units, activation="relu")ĭef call(self, inputs: tf.Tensor) -> Tuple: Refer to Gym's Cart Pole documentation page and Neuronlike adaptive elements that can solve difficult learning control problems by Barto, Sutton and Anderson (1983) for more information. The agent can take two actions to push the cart left ( 0) and right ( 1), respectively. The goal is to train a model that chooses actions based on a policy \(\pi\) that maximizes expected return.įor CartPole-v0, there are four values representing the state: cart position, cart-velocity, pole angle and pole velocity respectively. This tutorial uses model subclassing to define the model.ĭuring the forward pass, the model will take in the state as the input and will output both action probabilities and critic value \(V\), which models the state-dependent value function. The Actor and Critic will be modeled using one neural network that generates the action probabilities and Critic value respectively. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 01:24:58.393987: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 01:24:58.393975: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer_plugin.so.7' dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 01:24:58.393854: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer.so.7' dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory # Small epsilon value for stabilizing division operations # Set seed for experiment reproducibility pip install gym pip install pyglet # Install additional packages for visualization sudo apt-get install -y python-opengl > /dev/null 2>
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