# Tag Info

4

Parameters is a synonym for weights, which is the term most people use for a neural networks parameters (and indeed in my experience it is a term that machine learners will use in general whereas parameters is more often found in statistics literature). Batch size, learning rate etc. are hyper-parameters which basically means they are user specified, whereas ...

3

There is a really small mistake in here that causes the problem: for index, (current_state, action, reward, next_state, done) in enumerate(minibatch): if not done: new_q = reward + DISCOUNT * np.max(future_qs_list) #HERE else: new_q = reward # Update Q value for given ...

3

No, there is no file type associated with AI projects in general. Your examples of Photoshop and Excel are specific corporate branded products. These store bespoke data that only works with those products (plus maybe a few converters that can read the files for competitor products). Even more general examples such as .jpg for images or .txt for text ...

2

The main point in GPT-3 and already in 2 was the observation that performance was steadily increasing with increasing model size (As seen in Figure 1.2 in your linked paper). So it seems that while all progress made in NLP was definitely useful, it also is important to just scale up the model size. This may not seem like surprising point, but it actually ...

1

Is it training at all? Or is agent performance not improving over time? Q learning can be pretty unstable. I would recommend logging the sum of rewards received by the agent at the end of each episode and the model loss to help in the debugging process. The sum of rewards will show you if the agent is improving over time and the model loss will give you a ...

1

When it comes to GPU usage, nvidia-smi shows the usage at the time it was executed. You should try running watch -n0.01 nvidia-smi to see the usage of GPU every 0.01 second. It should output some small usage for current model, like 5%. You could try to increase you model, to e.g. self.d1 = Dense(1024, input_shape=(input_size,), activation="relu") ...

1

The question is conceptually wrong, because of misunderstanding of area. Explanation: The idea is to replace open ai gym by something different. For example: web-site or computer game. There is no way to create an environment based on image. If you want to use implemented algorithm for open ai gym and want to change environment for your own, could do ...

1

I don't recommend changing the rules of the environment. What you could do: Perform a method called bucketing i.e. take a value from a continuous state space see which discrete bucket it should go into and then let your agent use the bucket number as the observation. e.g. Say I do have a continuous state space with one variable in range $[-\infty,\infty]$ ...

1

If I understand your problem correctly, you can test on just about any environment, and just omit parts of the observations to ensure your RNN is learning. For example, you can test on cartpole, ignoring the velocity and angular velocity states. This way the MDP isn't actually Markovian and you'll need the RNN to learn.

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The video you linked is not using reinforcement learning (RL). It is using genetic algorithms (GA). GA is designed around using multiple agents and picking the best performing to move forward to next generation. With this approach, it is common to want to only view the best performing agents, as the learning mechanism uses the same selection process - the ...

1

I've actually implemented this game before using deep reinforcement learning. You are dealing with a dynamic action space here, where the action space may change at each time step of the game (or more generally the MDP). First, let's discuss the actual action spaces in each one of the two phases of Crib (or Cribbage) and formalize the question. Phase 1: The ...

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What I was looking for is multi-agent RL, where I have multiple RL agents, each controlling actions of one user. All RL agents/user make an action in each environment step and each get their own reward. I represent my RL agents' actions as dict, containing the RL agent ID as key and its action as value. The different agents may either use the same or a ...

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