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<a class="" href="../../algorithms/other/dfp/index.html">Direct Future Prediction</a>
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<a class="" href="../../algorithms/imitation/bc/index.html">Behavioral Cloning</a>
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<a class="current" href="./index.html">Adding a New Agent</a>
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<a class="" href="../add_env/index.html">Adding a New Environment</a>
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<a class="" href="../../algorithms/value_optimization/dqn/">DQN</a>
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</li>
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<li class="">
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<a class="" href="../../algorithms/value_optimization/double_dqn/">Double DQN</a>
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</li>
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<li class="">
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<a class="" href="../../algorithms/value_optimization/dueling_dqn/">Dueling DQN</a>
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</li>
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<li class="">
|
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<a class="" href="../../algorithms/value_optimization/categorical_dqn/">Categorical DQN</a>
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</li>
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<li class="">
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<a class="" href="../../algorithms/value_optimization/mmc/">Mixed Monte Carlo</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/value_optimization/pal/">Persistent Advantage Learning</a>
|
||||
</li>
|
||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/value_optimization/nec/">Neural Episodic Control</a>
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||||
</li>
|
||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/value_optimization/bs_dqn/">Bootstrapped DQN</a>
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||||
</li>
|
||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/value_optimization/n_step/">N-Step Q Learning</a>
|
||||
</li>
|
||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/value_optimization/naf/">Normalized Advantage Functions</a>
|
||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/policy_optimization/pg/">Policy Gradient</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/policy_optimization/ac/">Actor-Critic</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/policy_optimization/ddpg/">Deep Determinstic Policy Gradients</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/policy_optimization/ppo/">Proximal Policy Optimization</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/policy_optimization/cppo/">Clipped Proximal Policy Optimization</a>
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||||
</li>
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||||
<li class="">
|
||||
|
||||
<a class="" href="../../algorithms/other/dfp/">Direct Future Prediction</a>
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</li>
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||||
<li class="">
|
||||
|
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<a class="" href="../../algorithms/imitation/bc/">Behavioral Cloning</a>
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</li>
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</ul>
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<a class="current" href="./">Adding a New Agent</a>
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<a class="" href="../add_env/">Adding a New Environment</a>
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@@ -242,7 +189,7 @@
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<a href="../..">Reinforcement Learning Coach Documentation</a>
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<a href="../..">Reinforcement Learning Coach</a>
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</nav>
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@@ -273,42 +220,72 @@
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<p>Coach's modularity makes adding an agent a simple and clean task, that involves the following steps:</p>
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<ol>
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||||
<li>
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<p>Implement your algorithm in a new file under the agents directory. The agent can inherit base classes such as <strong>ValueOptimizationAgent</strong> or <strong>ActorCriticAgent</strong>, or the more generic <strong>Agent</strong> base class.</p>
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||||
<p>Implement your algorithm in a new file. The agent can inherit base classes such as <strong>ValueOptimizationAgent</strong> or
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<strong>ActorCriticAgent</strong>, or the more generic <strong>Agent</strong> base class.</p>
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||||
<ul>
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||||
<li>
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||||
<p><strong>ValueOptimizationAgent</strong>, <strong>PolicyOptimizationAgent</strong> and <strong>Agent</strong> are abstract classes.
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learn_from_batch() should be overriden with the desired behavior for the algorithm being implemented. If deciding to inherit from <strong>Agent</strong>, also choose_action() should be overriden. </p>
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<pre><code>def learn_from_batch(self, batch):
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<li><strong>ValueOptimizationAgent</strong>, <strong>PolicyOptimizationAgent</strong> and <strong>Agent</strong> are abstract classes.
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learn_from_batch() should be overriden with the desired behavior for the algorithm being implemented.
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If deciding to inherit from <strong>Agent</strong>, also choose_action() should be overriden.<pre><code>def learn_from_batch(self, batch) -> Tuple[float, List, List]:
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"""
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Given a batch of transitions, calculates their target values and updates the network.
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:param batch: A list of transitions
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:return: The loss of the training
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:return: The total loss of the training, the loss per head and the unclipped gradients
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"""
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pass
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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def choose_action(self, curr_state):
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"""
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choose an action to act with in the current episode being played. Different behavior might be exhibited when training
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or testing.
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:param curr_state: the current state to act upon.
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:param phase: the current phase: training or testing.
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:param curr_state: the current state to act upon.
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:return: chosen action, some action value describing the action (q-value, probability, etc)
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"""
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pass
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</code></pre>
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</li>
|
||||
<li>
|
||||
<p>Make sure to add your new agent to <strong>agents/__init__.py</strong></p>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<p>Implement your agent's specific network head, if needed, at the implementation for the framework of your choice. For example <strong>architectures/neon_components/heads.py</strong>. The head will inherit the generic base class Head.
|
||||
A new output type should be added to configurations.py, and a mapping between the new head and output type should be defined in the get_output_head() function at <strong>architectures/neon_components/general_network.py</strong></p>
|
||||
<p>Implement your agent's specific network head, if needed, at the implementation for the framework of your choice.
|
||||
For example <strong>architectures/neon_components/heads.py</strong>. The head will inherit the generic base class Head.
|
||||
A new output type should be added to configurations.py, and a mapping between the new head and output type should
|
||||
be defined in the get_output_head() function at <strong>architectures/neon_components/general_network.py</strong></p>
|
||||
</li>
|
||||
<li>
|
||||
<p>Define a new parameters class that inherits AgentParameters.
|
||||
The parameters class defines all the hyperparameters for the agent, and is initialized with 4 main components:</p>
|
||||
<ul>
|
||||
<li><strong>algorithm</strong>: A class inheriting AlgorithmParameters which defines any algorithm specific parameters</li>
|
||||
<li><strong>exploration</strong>: A class inheriting ExplorationParameters which defines the exploration policy parameters.
|
||||
There are several common exploration policies built-in which you can use, and are defined under
|
||||
the exploration sub directory. You can also define your own custom exploration policy.</li>
|
||||
<li><strong>memory</strong>: A class inheriting MemoryParameters which defined the memory parameters.
|
||||
There are several common memory types built-in which you can use, and are defined under the memories
|
||||
sub directory. You can also define your own custom memory.</li>
|
||||
<li><strong>networks</strong>: A dictionary defining all the networks that will be used by the agent. The keys of the dictionary
|
||||
define the network name and will be used to access each network through the agent class.
|
||||
The dictionary values are a class inheriting NetworkParameters, which define the network structure
|
||||
and parameters.</li>
|
||||
</ul>
|
||||
<p>Additionally, set the path property to return the path to your agent class in the following format:</p>
|
||||
<pre><code> <path to python module>:<name of agent class>
|
||||
</code></pre>
|
||||
<p>For example,</p>
|
||||
<pre><code> class RainbowAgentParameters(AgentParameters):
|
||||
def __init__(self):
|
||||
super().__init__(algorithm=RainbowAlgorithmParameters(),
|
||||
exploration=RainbowExplorationParameters(),
|
||||
memory=RainbowMemoryParameters(),
|
||||
networks={"main": RainbowNetworkParameters()})
|
||||
|
||||
@property
|
||||
def path(self):
|
||||
return 'rainbow.rainbow_agent:RainbowAgent'
|
||||
</code></pre>
|
||||
</li>
|
||||
<li>
|
||||
<p>(Optional) Define a preset using the new agent type with a given environment, and the hyper-parameters that should
|
||||
be used for training on that environment.</p>
|
||||
</li>
|
||||
<li>Define a new configuration class at configurations.py, which includes the new agent name in the <strong>type</strong> field, the new output type in the <strong>output_types</strong> field, and assigning default values to hyperparameters.</li>
|
||||
<li>(Optional) Define a preset using the new agent type with a given environment, and the hyperparameters that should be used for training on that environment.</li>
|
||||
</ol>
|
||||
|
||||
</div>
|
||||
@@ -317,10 +294,10 @@ def choose_action(self, curr_state, phase=RunPhase.TRAIN):
|
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<a class="" href="../../algorithms/value_optimization/bs_dqn/">Bootstrapped DQN</a>
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<a class="current" href="./">Adding a New Environment</a>
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<li class="toctree-l3"><a href="#using-the-openai-gym-api">Using the OpenAI Gym API</a></li>
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<li class="toctree-l3"><a href="#using-the-coach-api">Using the Coach API</a></li>
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@@ -269,74 +222,81 @@
|
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<div class="section">
|
||||
|
||||
<p>Adding a new environment to Coach is as easy as solving CartPole. </p>
|
||||
<p>There are essentially two ways to integrate new environments to Coach:</p>
|
||||
<h2 id="using-the-openai-gym-api">Using the OpenAI Gym API</h2>
|
||||
<p>If your environment is already using the OpenAI Gym API, you are already good to go.
|
||||
When selecting the environment parameters in the preset, use GymEnvironmentParameters(),
|
||||
and pass the path to your environment source code using the level parameter.
|
||||
You can specify additional parameters for your environment using the additional_simulator_parameters parameter.
|
||||
Take for example the definition used in the Pendulum_HAC preset:</p>
|
||||
<pre><code> env_params = GymEnvironmentParameters()
|
||||
env_params.level = "rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals"
|
||||
env_params.additional_simulator_parameters = {"time_limit": 1000}
|
||||
</code></pre>
|
||||
<h2 id="using-the-coach-api">Using the Coach API</h2>
|
||||
<p>There are a few simple steps to follow, and we will walk through them one by one.</p>
|
||||
<ol>
|
||||
<li>
|
||||
<p>Coach defines a simple API for implementing a new environment which is defined in environment/environment_wrapper.py.
|
||||
There are several functions to implement, but only some of them are mandatory. </p>
|
||||
<p>Create a new class for your environment, and inherit the Environment class.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p>Coach defines a simple API for implementing a new environment, which are defined in environment/environment.py.
|
||||
There are several functions to implement, but only some of them are mandatory.</p>
|
||||
<p>Here are the important ones:</p>
|
||||
<pre><code> def _take_action(self, action_idx):
|
||||
<pre><code> def _take_action(self, action_idx: ActionType) -> None:
|
||||
"""
|
||||
An environment dependent function that sends an action to the simulator.
|
||||
:param action_idx: the action to perform on the environment.
|
||||
:param action_idx: the action to perform on the environment
|
||||
:return: None
|
||||
"""
|
||||
pass
|
||||
|
||||
def _preprocess_observation(self, observation):
|
||||
"""
|
||||
Do initial observation preprocessing such as cropping, rgb2gray, rescale etc.
|
||||
Implementing this function is optional.
|
||||
:param observation: a raw observation from the environment
|
||||
:return: the preprocessed observation
|
||||
"""
|
||||
return observation
|
||||
|
||||
def _update_state(self):
|
||||
def _update_state(self) -> None:
|
||||
"""
|
||||
Updates the state from the environment.
|
||||
Should update self.observation, self.reward, self.done, self.measurements and self.info
|
||||
:return: None
|
||||
"""
|
||||
pass
|
||||
|
||||
def _restart_environment_episode(self, force_environment_reset=False):
|
||||
def _restart_environment_episode(self, force_environment_reset=False) -> None:
|
||||
"""
|
||||
Restarts the simulator episode
|
||||
:param force_environment_reset: Force the environment to reset even if the episode is not done yet.
|
||||
:return:
|
||||
:return: None
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_rendered_image(self):
|
||||
def _render(self) -> None:
|
||||
"""
|
||||
Renders the environment using the native simulator renderer
|
||||
:return: None
|
||||
"""
|
||||
|
||||
def get_rendered_image(self) -> np.ndarray:
|
||||
"""
|
||||
Return a numpy array containing the image that will be rendered to the screen.
|
||||
This can be different from the observation. For example, mujoco's observation is a measurements vector.
|
||||
:return: numpy array containing the image that will be rendered to the screen
|
||||
"""
|
||||
return self.observation
|
||||
</code></pre>
|
||||
</li>
|
||||
<li>
|
||||
<p>Make sure to import the environment in environments/__init__.py:</p>
|
||||
<pre><code>from doom_environment_wrapper import *
|
||||
</code></pre>
|
||||
<p>Also, a new entry should be added to the EnvTypes enum mapping the environment name to the wrapper's class name:</p>
|
||||
<pre><code>Doom = "DoomEnvironmentWrapper"
|
||||
<p>Create a new parameters class for your environment, which inherits the EnvironmentParameters class.
|
||||
In the <strong>init</strong> of your class, define all the parameters you used in your Environment class.
|
||||
Additionally, fill the path property of the class with the path to your Environment class.
|
||||
For example, take a look at the EnvironmentParameters class used for Doom:</p>
|
||||
<pre><code> class DoomEnvironmentParameters(EnvironmentParameters):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.default_input_filter = DoomInputFilter
|
||||
self.default_output_filter = DoomOutputFilter
|
||||
self.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
|
||||
|
||||
@property
|
||||
def path(self):
|
||||
return 'rl_coach.environments.doom_environment:DoomEnvironment'
|
||||
</code></pre>
|
||||
</li>
|
||||
<li>
|
||||
<p>In addition a new configuration class should be implemented for defining the environment's parameters and placed in configurations.py.
|
||||
For instance, the following is used for Doom:</p>
|
||||
<pre><code>class Doom(EnvironmentParameters):
|
||||
type = 'Doom'
|
||||
frame_skip = 4
|
||||
observation_stack_size = 3
|
||||
desired_observation_height = 60
|
||||
desired_observation_width = 76
|
||||
</code></pre>
|
||||
</li>
|
||||
<li>
|
||||
<p>And that's it, you're done. Now just add a new preset with your newly created environment, and start training an agent on top of it. </p>
|
||||
<p>And that's it, you're done. Now just add a new preset with your newly created environment, and start training an agent on top of it.</p>
|
||||
</li>
|
||||
</ol>
|
||||
|
||||
@@ -347,7 +307,7 @@ For instance, the following is used for Doom:</p>
|
||||
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@@ -361,7 +321,7 @@ For instance, the following is used for Doom:</p>
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