Reinforcement_Learning | Reinforcement learning tutorials | Reinforcement Learning library
kandi X-RAY | Reinforcement_Learning Summary
kandi X-RAY | Reinforcement_Learning Summary
Reinforcement learning tutorials
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Top functions reviewed by kandi - BETA
- Run multiprocessing
- Gaussian likelihood
- Predict the next action
- Plot a model
- Run the optimizer
- Store an experience
- Play the prediction
- Compute the action of the given state
- Train a thread
- Forward an action
- Reset the image
- Gets an image
- Run the game
- Plot the model
- Run the test
- Display frames as gif
- Computes the Poisson objective loss
- Compute the gaussian log likelihood
- Add an experience to the forest
- Update the priority score
- Train the environment
- Test the model
- Generate random games
- Train the game
- Run a single batch
Reinforcement_Learning Key Features
Reinforcement_Learning Examples and Code Snippets
Community Discussions
Trending Discussions on Reinforcement_Learning
QUESTION
I write some tensorflow code about Deep Successor Representation (DSQ) reinforcement learning:
...ANSWER
Answered 2021-Jan-15 at 08:07A call to the optimizer must be out of the scope of the gradient tape, i.e:
QUESTION
I'm using rl coach through AWS Sagemaker, and I'm running in an issue that I struggle to understand.
I'm performing RL using AWS Sagemaker for the learning, and AWS Robomaker for the environment, like in DeepRacer which uses rl coach as well. In fact, the code only little differs with the DeepRacer code on the learning side. But the environment is completely different though.
What happens:
- The graph manager initialization succeeds
- A first checkpoint is generated (and uploaded to S3)
- The agent loads the first checkpoint
- The agent performs N episodes with the first policy
- The graph manager fetches the N episodes
- The graph manager performs 1 training step and create a second checkpoint (uploaded to S3)
- The agent fails to restore the model with the second checkpoint.
The agent raises an exception with the message : Failed to restore agent's checkpoint: 'main_level/agent/main/online/global_step'
The traceback points to a bug happening in this rl coach module:
...ANSWER
Answered 2020-Oct-17 at 11:54I removed the patch (technically I removed the patch command in my dockerfile that was applying it), and now it works, the model is correctly restored from the checkpoint.
QUESTION
I have a mismatch in shapes between inputs and the model of my reinforcement learning project.
I have been closely following the AWS examples, specifically the cartpole example. However I have built my own custom environment. What I am struggling to understand is how to change my environment so that it is able to work with the prebuilt Ray RLEstimator.
Here is the code for the environment:
...ANSWER
Answered 2019-Sep-18 at 20:19Possible reason:
The error message:
ValueError: Input 0 of layer default/fc1 is incompatible with the layer: : expected min_ndim=2, found ndim=1. Full shape received: [None]
Your original environment obs space is self.observation_space = Box(np.array(0.0),np.array(1000)).
Displaying the shape of your environment obs space gives:
print(Box(np.array(0.0), np.array(1000), dtype=np.float32).shape) = ()
This could be indicated by Full shape received: [None] in the error message.
If you pass the shape (1,1) into np.zeros, you get the expected min_ndim=2:
x = np.zeros((1, 1))
print(x)
[[0.]]
print(x.ndim)
2
Suggested solution:
I assume that you want your environment obs space to range from 0.0 to 1000.0 as indicated by the self.price = np.random.rand() in your reset function.
Try using the following for your environment obs space:
self.observation_space = Box(0.0, 1000.0, shape=(1,1), dtype=np.float32)
I hope that by setting the Box with an explicit shape helps.
EDIT (20190903):
I have modified your training script. This modification includes new imports, custom model class, model registration & addition of registered custom model to config. For readability, only sections added are shown below. The entire modified training script is available in this gist. Please run with the proposed obs space as describe above.
New additional imports:
QUESTION
I have a simple pytorch neural net that I copied from openai, and I modified it to some extent (mostly the input).
When I run my code, the output of the network remains the same on every episode, as if no training occurs.
I want to see if any training happens, or if some other reason causes the results to be the same.
How can I make sure any movement happens to the weights?
Thanks
...ANSWER
Answered 2019-Feb-27 at 00:10Depends on what you are doing, but the easiest would be to check the weights of your model.
You can do this (and compare with the ones from previous iteration) using the following code:
QUESTION
When trying to create a neural network and optimize it using Pytorch, I am getting
ValueError: optimizer got an empty parameter list
Here is the code.
...ANSWER
Answered 2019-Feb-14 at 06:29Your NetActor does not directly store any nn.Parameter. Moreover, all other layers it eventually uses in forward are stored as a simple list is self.nn_layers.
If you want self.actor_nn.parameters() to know that the items stored in the list self.nn_layers may contain trainable parameters, you should work with containers.
Specifically, making self.nn_layers to be a nn.ModuleList instead of a simple list should solve your problem:
QUESTION
OpenAI's REINFORCE and actor-critic example for reinforcement learning has the following code:
...ANSWER
Answered 2019-Jan-22 at 11:31stack
Concatenates sequence of tensors along a new dimension.
cat
Concatenates the given sequence of seq tensors in the given dimension.
So if A and B are of shape (3, 4), torch.cat([A, B], dim=0) will be of shape (6, 4) and torch.stack([A, B], dim=0) will be of shape (2, 3, 4).
QUESTION
[Introduction] I'm a beginner with OpenAI, I have made a custom game into which I would like to implement a self-learning agent. I followed this guide to set up a repository on GitHub, however I do not understand how I could format my code to work with the contents of gym-foo/gym_foo/envs/foo_env.py
[Question] Is there any chance someone could guide me on how to structure my code to so it’s compatible with:
...ANSWER
Answered 2018-Apr-05 at 17:58I have no experience with the pygame library and no knowledge of its internal workings, that may have some influence on what code needs to run where, so I'm not 100% sure on all of that. But, it's good to just start with some intuitive understanding of roughly what should be happening where:
__init__()should run any one-time setup. I can imagine something likepygame.init()may have to go in here, but this I'm not 100% sure on because I'm not familiar withpygame.step()should be called whenever an agent selects an action, and then run a single ''frame'' of the game, move it forwards given the action selected by the agent. Alternatively, if you have a game where a single action takes multiple frames, you should run multiple frames here. Essentially: keep the game moving forwards until you hit a point where the agent should get to choose a new action again, then return the current game state.reset()should... well, reset the game. So, revert back to the (or a random, whatever you want) initial game state, run any cleanup that may be required. I could, for example, also imaginepygame.init()belonging in here. It depends on what exactly that function does. If it only needs to be run once, it belongs in__init__(). If it needs to run at the start of every new game/"episode", ir belongs inreset().render()should probably contain most of your graphics related code. You can try to take inspiration from, for example, the cartpole environment in gym, which also draws some rather simple graphics here. It looks like it should draw exactly one frame.
Now, looking at the code you're starting from, there seems to be a signifant amount of User Interface code... all kinds of code related to buttons, pausing/unpausing, a fancy (animated?) intro at the start of the game. I don't know if you can afford to get rid of all this? If you're doing purely Reinforcement Learning, you probably can. If you still need user interaction, you probably can't, and then things become a whole lot more difficult since all these things do not nicely fit the gym framework.
I can try to make a few educated guesses of a few of the remaining parts of the code and where it should go, but you should carefully inspect everything anyway based on the more general guidelines above:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install Reinforcement_Learning
You can use Reinforcement_Learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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