kandi X-RAY | tensorforce Summary
kandi X-RAY | tensorforce Summary
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of Google's TensorFlow framework and requires Python 3.
Top functions reviewed by kandi - BETA
- Runs the network
- Close the agent
- Evaluate the agent
- Handles the action
- Evaluate the evaluation
- Return an iterator over the values
- Create a new tracking module
- Map a function over the NestedDict
- Updates the tensorflow
- Returns signature for given function
- Compute the state values for each state
- Calculate parameter value
- Compute action entropy
- Apply the policy
- Step through the input function
- Compute the policy
- Decorator for functions
- Estimates the agent
- Calculate a single step
- Perform core act on policy
- Performs the act on the agent
- Compute the action values
- Observe the interaction
- Enqueues the given state
- Computes the action function
- Perform a single step
tensorforce Key Features
tensorforce Examples and Code Snippets
from tensorforce.contrib.unreal_engine import UE4Environment import random if __name__ == "__main__": environment = UE4Environment(host="localhost", port=6025, connect=True, discretize_actions=True, num_ticks=6) environment.seed(200) #
from tensorforce.agents import VPGAgent from tensorforce.agents import DQNAgent [...] agent = VPGAgent(states_spec=dict(shape=state_dim, type='float'), actions_spec=dict(num_actions=action_space, type='int'),
from helper.templates import Agent class DoNothingAgent(Agent): """ An agent that chooses NOOP action at every timestep. """ def __init__(self, observation_space, action_space): self.action =  * action_space.shape
from tensorforce.core.layers import Dense d = Dense(size=4)
config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config=config)
config = tf.ConfigProto() # config.gpu_options.allow_growth = True config.gpu_options.per_process_gpu_memory
Trending Discussions on tensorforce
I am currently trying to understand the Tensorforce library . I keep stumbling across a signature in the form:...
ANSWERAnswered 2021-May-29 at 02:05
Any arguments specified after the
* "argument" (so in this case, all of them) are keyword-only arguments. They can only be supplied by keyword, rather than positionally; this means your example should be:
I know this is a silly question, but I cannot find a good way to put it.
I've worked with TensorFlow and TFAgents, and am now moving to Ray RLlib. Looking at all the RL frameworks/libraries, I got confused about the difference between the two below:
- frameworks such as Keras, TensorFlow, PyTorch
- RL implementation libraries such as TFAgents, RLlib, OpenAi Baseline, Tensorforce, KerasRL, etc
For example, there are Keras codes in TensorFlow and Ray RLlib supports both TensorFlow and PyTorch. How are they all related?
My understanding so far is that Keras allows to make neural networks and TensorFlow is more of a math library for RL (I don't have enough understanding about PyTorch). And libraries like TFAgents and RLlib use frameworks like Keras and TensorFlow to implement existing RL algorithms so that programmers can utilize them with ease.
Can someone please explain how they are interconnected/different? Thank you very much....
ANSWERAnswered 2020-Dec-15 at 09:48
Yes you are kind of right. Frameworks like Keras, TF (which also uses keras btw) and Pytorch are general Deep Learning frameworks. For most artificial neural network use-cases these frameworks work just fine and your typical pipeline is going to look something like:
- Preprocess your dataset
- Select an appropriate model for this problem setting
- Analyze results
Reinforcement Learning though is substantially different from most other Data Science ML applications. To start with, in RL you actually generate your own dataset by having your model (the Agent) interact with an environment; this complicates the situation substantially particularly from a computational standpoint. This is because in the traditional ML scenario most of the computational heavy-lifting is done by that model.fit() call. And the good thing about the aforementioned frameworks is that from that call your code actually enters very efficient C/C++ code (usually also implementing CUDA libraries to use the GPU).
In RL the big problem is the environment that the agent interacts with. I separate this problem in two parts:
a) The environment cannot be implemented in these frameworks because it will always change based on what you are doing. As such you have to code the environment and - chances are - it's not gonna be very efficient.
b) The environment is a key component in the code and it constantly intreacts multiple times with your Agent, and there are multiple ways in which that interaction can be mediated.
These two factors lead to the necessity to standardize the environment and the interaction between it and the agent. This standardization allows for highly reusable code and also code that is more interpretable by others in how it exactly operates. Furthermore it is possible this way to, for example, easily run parallel environments (TF-agents allows this for example) even though your environment object is not really written to manage this.
RL frameworks are thus providing this standardization and features that come with it. Their relation to Deep Learning frameworks is that RL libraries often come with a lot of pre-implemented and flexible agent architectures that have been among the most relevant in the literature. These agents are usually nothing more than a some fancy ANN architecture wrapped in some class that standardizes their operation within the given RL framework. Therefore as a backend for these ANN models, RL frameworks use DL frameworks to run the computations efficiently.
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