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keep using tf-keras rather than keras-3 which is not compatible.
Also, moving tensorflow from [reverb] requirement to main requirments. PiperOrigin-RevId: 592609539 Change-Id: Ife593ab7960dddbd545d33eb9a49dce60e27f1f6
1 parent e5219db commit c58ea0c

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-21
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‎README.md

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```shell
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$ pip install --user tf-agents[reverb]
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# Use keras-2
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$ export TF_USE_LEGACY_KERAS=1
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# Use this tag get the matching examples and colabs.
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$ git clone https://github.com/tensorflow/agents.git
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$ cd agents
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```shell
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$ pip install --user tensorflow
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$ pip install --user tf-keras
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$ pip install --user dm-reverb
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$ pip install --user tf-agents
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```
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To install the nightly build version, run the following:
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```shell
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# Use keras-2
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$ export TF_USE_LEGACY_KERAS=1
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# `--force-reinstall helps guarantee the right versions.
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$ pip install --user --force-reinstall tf-nightly
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$ pip install --user --force-reinstall tf-keras-nightly
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$ pip install --user --force-reinstall tfp-nightly
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$ pip install --user --force-reinstall dm-reverb-nightly
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‎docs/overview.md

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```shell
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$ pip install --user tf-agents[reverb]
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# Use keras-2
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$ export TF_USE_LEGACY_KERAS=1
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# Use this tag get the matching examples and colabs.
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$ git clone https://github.com/tensorflow/agents.git
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$ cd agents
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```shell
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$ pip install --user tensorflow
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$ pip install --user tf-keras
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$ pip install --user dm-reverb
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$ pip install --user tf-agents
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```
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To install the nightly build version, run the following:
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```shell
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# Use keras-2
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$ export TF_USE_LEGACY_KERAS=1
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# `--force-reinstall helps guarantee the right versions.
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$ pip install --user --force-reinstall tf-nightly
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$ pip install --user --force-reinstall tf-keras-nightly
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$ pip install --user --force-reinstall tfp-nightly
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$ pip install --user --force-reinstall dm-reverb-nightly
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‎docs/tutorials/10_checkpointer_policysaver_tutorial.ipynb

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"!pip install tf-agents[reverb]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "WPuD0bMEY9Iz"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎docs/tutorials/1_dqn_tutorial.ipynb

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"!pip install pyglet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "UX0aSKBCYmj2"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"\n",
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"In Reinforcement Learning (RL), an environment represents the task or problem to be solved. Standard environments can be created in TF-Agents using `tf_agents.environments` suites. TF-Agents has suites for loading environments from sources such as the OpenAI Gym, Atari, and DM Control.\n",
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"\n",
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"Load the CartPole environment from the OpenAI Gym suite. "
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"Load the CartPole environment from the OpenAI Gym suite."
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]
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},
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{
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"source": [
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"In the Cartpole environment:\n",
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"\n",
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"- `observation` is an array of 4 floats: \n",
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"- `observation` is an array of 4 floats:\n",
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" - the position and velocity of the cart\n",
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" - the angular position and velocity of the pole \n",
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" - the angular position and velocity of the pole\n",
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"- `reward` is a scalar float value\n",
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"- `action` is a scalar integer with only two possible values:\n",
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" - `0` — \"move left\"\n",
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"id": "4JSc9GviWUBK"
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},
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"source": [
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"Usually two environments are instantiated: one for training and one for evaluation. "
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"Usually two environments are instantiated: one for training and one for evaluation."
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]
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{
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"- The desired outcome is keeping the pole balanced upright over the cart.\n",
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"- The policy returns an action (left or right) for each `time_step` observation.\n",
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"\n",
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"Agents contain two policies: \n",
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"Agents contain two policies:\n",
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"\n",
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"- `agent.policy` — The main policy that is used for evaluation and deployment.\n",
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"- `agent.collect_policy` — A second policy that is used for data collection.\n"
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"source": [
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"# For the curious:\n",
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"# Uncomment to see what the dataset iterator is feeding to the agent.\n",
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"# Compare this representation of replay data \n",
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"# Compare this representation of replay data\n",
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"# to the collection of individual trajectories shown earlier.\n",
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"\n",
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"# iterator.next()"
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"id": "9pGfGxSH32gn"
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},
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"source": [
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"Charts are nice. But more exciting is seeing an agent actually performing a task in an environment. \n",
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"Charts are nice. But more exciting is seeing an agent actually performing a task in an environment.\n",
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"\n",
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"First, create a function to embed videos in the notebook."
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "DQN Tutorial.ipynb",
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"private_outputs": true,
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"provenance": [],

‎docs/tutorials/2_environments_tutorial.ipynb

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"!pip install tf-agents[reverb]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "WPuD0bMEY9Iz"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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]
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},
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"The interface for TF environments is defined in `environments/tf_environment.TFEnvironment` and looks very similar to the Python environments. TF Environments differ from Python envs in a couple of ways:\n",
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"\n",
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"* They generate tensor objects instead of arrays\n",
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"* TF environments add a batch dimension to the tensors generated when compared to the specs. \n",
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"* TF environments add a batch dimension to the tensors generated when compared to the specs.\n",
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"\n",
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"Converting the Python environments into TFEnvs allows tensorflow to parallelize operations. For example, one could define a `collect_experience_op` that collects data from the environment and adds to a `replay_buffer`, and a `train_op` that reads from the `replay_buffer` and trains the agent, and run them in parallel naturally in TensorFlow."
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]
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],
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‎docs/tutorials/3_policies_tutorial.ipynb

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"!pip install tf-agents"
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{
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"cell_type": "code",
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"metadata": {
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"id": "h3dkwi09ZQeJ"
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},
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"outputs": [],
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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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]
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},
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"outputs": [],
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"action_spec = array_spec.BoundedArraySpec((2,), np.int32, -10, 10)\n",
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"action_script = [(1, np.array([5, 2], dtype=np.int32)), \n",
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"action_script = [(1, np.array([5, 2], dtype=np.int32)),\n",
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" (0, np.array([0, 0], dtype=np.int32)), # Setting `num_repeats` to 0 will skip this action.\n",
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"Note that in the above, actions are clipped to the range of the given action spec [-1, 1]. This is because a constructor argument of ActorPolicy clip=True by default. Setting this to false will return unclipped actions produced by the network."
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"A policy wrapper can be used to wrap and modify a given policy, e.g. add noise. Policy wrappers are a subclass of Policy (Python/TensorFlow) and can therefore be used just like any other policy."
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‎docs/tutorials/4_drivers_tutorial.ipynb

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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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‎docs/tutorials/5_replay_buffers_tutorial.ipynb

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"import os\n",
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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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‎docs/tutorials/6_reinforce_tutorial.ipynb

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"# Keep using keras-2 (tf-keras) rather than keras-3 (keras).\n",
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"os.environ['TF_USE_LEGACY_KERAS'] = '1'"
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‎docs/tutorials/7_SAC_minitaur_tutorial.ipynb

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‎docs/tutorials/8_networks_tutorial.ipynb

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‎docs/tutorials/9_c51_tutorial.ipynb

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