Questions tagged [deep-rl]

For questions related to deep reinforcement learning (DRL), that is, RL combined with deep learning. More precisely, deep neural networks are used to represent e.g. value functions or policies.

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1answer
91 views

beautify an image with reinforcement learning

I am trying to formulate and solve the following problem of image mutation. Suppose I am trying to insert an object image into a "background" image of several objects, and I will need to look for a "...
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0answers
43 views

How does the TRPO surrogate loss account for the error in the policy?

In the Trust Region Policy Optimization (TRPO) paper, on page 10, it is stated An informal overview is as follows. Our proof relies on the notion of coupling, where we jointly define the ...
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255 views

Do we need to use the experience replay buffer with the A3C algorithm?

I have skimmed through a bunch of deep learning books, but I have not yet understood whether we must use the experience replay buffer with the A3C algorithm. The approached I used is the following: ...
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31 views

What are a list of board game environments for RL practice?

Recently OpenAI removes their board game environments. (It may be possible to install an older version to get access to them, but I haven’t downgraded). Is there a list of repositories or resources ...
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0answers
213 views

Why overfitting is bad in DQN?

It is mentioned by Fu 2019 that overfitting might have a negative effect on training DQN. They showed that with either early stopping or experience replay this effect could be reduced. The first is ...
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0answers
100 views

Why experience reply memory in DQN instead of a RNN memory?

I was trying to implement a DQN without experience reply memory, and the agent is not learning anything at all. I know from readings that experience reply is used for stabilizing gradients. But how ...
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0answers
162 views

DQN Agent not learning anymore - what can I do to fix this?

I am trying to use Deep-Q-Learning to learn an ANN which controls a 7-DOF robotic arm. The robotic arm must avoid an obstacle and reach a target. I have implemented a number of state-of-art ...
5
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2answers
328 views

Why don't people use projected Bellman error with deep neural networks?

Projected Bellman error has shown to be stable with linear function approximation. The technique is not at all new. I can only wonder why this technique is not adopted to use with non-linear function ...
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49 views

Deep Q-Learning agent poor performing actions. Need help optimizing

I'm trying to make deep q-learning agent from https://keon.io/deep-q-learning My environment looks like this: https://imgur.com/a/OnbiCtV As you can see my agent is a circle and there is one gray ...
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163 views

What can be considered a deep recurrent neural network?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the DRQN is described as DQN with the first post-convolutional fully-connected layer replaced by a recurrent LSTM. I have DQN ...
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2answers
297 views

Can DQN perform better than Double DQN?

I'm training both DQN and double DQN in the same environment, but DQN performs significantly better than double DQN. As I've seen in the double DQN paper, double DQN should perform better than DQN. Am ...
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3answers
1k views

Why doesn't Q-learning converge when using function approximation?

The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied $\...
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2answers
3k views

How large should the replay buffer be?

I'm learning DDPG algorithm by following the following link: Open AI Spinning Up document on DDPG, where it is written In order for the algorithm to have stable behavior, the replay buffer should ...
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2answers
369 views

Is reinforcement learning using shallow neural networks still deep reinforcement learning?

Often times I see the term deep reinforcement learning to refer to RL algorithms that use neural networks, regardless of whether or not the networks are deep. For example, PPO is often considered a ...
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303 views

DQN Q-mean values converge negatively

I'm trying to implement my own DQN. So far I think my code is good, but my Q-values (I'm getting the mean of all the values for every episode) tends to converge near-zero but negatively. It is normal? ...
2
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1answer
239 views

Regarding the output layer's activation function for continuous action space problems

I'm interested in building a (deep) RL agent for solving a continuous problem (which splits something into portions). In all examples I've seen so far, e.g., solving the continuous lunar lander, ...
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103 views

How did the OpenAI 5 for Dota concatenate units?

I am no expert in the field of AI so I apologize if this is a simple/easy question. I was trying to implement a network similar to OpenAI's for another game and I noticed that I did not fully ...
3
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1answer
100 views

Why Q2 is a more or less independant estimate in Twin Delayed DDPG (TD3)?

Twin Delayed Deep Deterministic (TD3) policy gradient is inspired by both double Q-learning and double DQN. In double Q-learning, I understand that Q1 and Q2 are independent because they are trained ...
3
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1answer
332 views

What are the differences between the DQN variants?

There are several variants of the DQN model. For example, double DQN, duelling DQN, prioritized DQN, distributed prioritized DQN, episodic memory DQN, asynchronous n-step DQN and multiple DQN. What ...
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0answers
38 views

How does Hindsight Experience Replay cope with multiple goals?

What if there are multiple goals? For example, let's consider the bit-flipping environment as described in the paper HER with one small change: now, the goal is not some specific configuration, but ...
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21 views

How to serve a deep q network using tensorflow serving?

How to Serve a Deep Q Network using Tensorflow Serving. I have built a Deep Q Network using Multilayer Perceptron. Is it possible to serve it?
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0answers
37 views

Comparison and understanding of different version of DDQN?

There are several version of DDQN floating around. Sutton gives one that is a simple symmetric random update of the two Q functions. I think other papers (Silver paper for example) use a kind of ...
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2answers
992 views

Do we have to use CNN for Deep Q Learning?

I read top articles on Google Search about Deep Q-Learning: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8 https://skymind.ai/wiki/deep-reinforcement-...
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1answer
237 views

Is there a way to train an RL agent without any environment?

Following Deep Q-learning from Demonstrations, I'd like to avoid potentially unsafe behavior during early learning by making use of supervised learning with demonstration data. However, the ...
2
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1answer
2k views

In DQN, updating target network every N steps or slowly update every step is better?

The use of target network is to reduce the chance of value divergence which could happen with off-policy samples trained with semi-gradient objectives. In Deep Q network, semi-gradient TD is used and ...
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2answers
202 views

Is there an alternative to the use of target network?

In the context of Deep Q Network, a target network is usually utilized. The target network is a slow changing network with a changing rate as its hyperparameter. This includes both replacement update ...
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2answers
1k views

What is the difference between DQN and AlphaGo Zero?

I have already implemented a relatively simple DQN on Pacman. Now I would like to clearly understand the difference between a DQN and the techniques used by AlphaGo zero/AlphaZero and I couldn't find ...
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2answers
1k views

My DQN is stuck and can't see where the problem is

I'm trying to replicate the DeepMind paper results, so I implemented my own DQN. I left it training for more than 4 million frames (more than 2000 episodes) on SpaceInvaders-v4 (OpenAI-Gym) and it ...
2
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1answer
357 views

Why does Deep Q Network outputs multiple Q values?

I am learning Deep RL following this tutorial: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8 I understand everything but one detail: This image shows ...
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1answer
86 views

Deep Q-learning is not performing well when there are several enemies

I am playing with a deep Q-learning algorithm in my own environment. The network can perform well as long as there is only one enemy. My agent can perform the following actions: ...
6
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1answer
1k views

Can TD($\lambda$) be used with deep reinforcement learning?

TD lambda is a way to interpolate between TD(0) - bootstrapping over a single step, and, TD(max), bootstrapping over the entire episode length, or, Monte Carlo. Reading the link above, I see that an ...
2
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1answer
66 views

Is there a relation between the size of the neural networks and speed of convergence in deep reinforcement learning?

Is there a connection between the approximator network sizes in a RL task and the speed of convergence to an (near) optimal policy or value function? When thinking about this, I came across the ...
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1answer
53 views

How do I calculate $max_{a′}Q(s′,a′,w−)$ when it is represented as a neural network?

Consider the following loss function $$ L(\mathbf{w}) = [(r + \gamma max_{a'} Q(s', a', \mathbf{w^-})) - Q(s, a, \mathbf{w})]^2 $$ where $Q(s, a, \mathbf{w^-})$ and $Q(s, a, \mathbf{w})$ are ...
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1answer
224 views

How do I update the Q values of a Deep Q Network when exploring?

I am trying to implement a Deep Q Network to play Asteroids. Unfortunately, I am not sure how to calculate the Q value exactly, if I am exploring. For example, the agent is exploring for 1 second (...
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1answer
125 views

Understanding lemma 2 of the “Trust Region Policy Optimization” paper

In the Trust Region Policy Optimization paper, in Lemma 2 of Appendix A, I did not quite understand deriving inequality (31) from (30), which is: $$\bar{A}(s) = P(a \neq \tilde{a} | s) \mathbb{E}_{(a,...
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1answer
165 views

How does Hindsight Experience Replay learn from unsuccessful trajectories?

I am confused by how HER learns from unsuccessful trajectories. I understand that from failed trajectories it creates 'fake' goals that it can learn from. Ignoring HER for now, if in the case where ...
5
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1answer
61 views

In imitation learning, do you simply inject optimal tuples of experience $(s, a, r, s')$ into your experience replay buffer?

Due to my RL algorithm having difficulties learning some control actions, I've decided to use imitation learning/apprenticeship learning to guide my RL to perform the optimal actions. I've read a few ...
14
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1answer
11k views

How does LSTM in deep reinforcement learning differ from experience replay?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are: How does this method differ from the ...
6
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2answers
735 views

Why is the log probability replaced with the importance sampling in the loss function?

In the Trust-Region Policy Optimisation (TRPO) algorithm (and subsequently in PPO also), I do not understand the motivation behind replacing the log probability term from standard policy gradients $$L^...
3
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1answer
52 views

Maximizing or Minimizing in Trust Region Policy Optimization?

I happened to discover that the v1 (19 Feb 2015) and the v5 (20 Apr 2017) versions of TRPO papers have two different conclusions. The Equation (15) in v1 is $\min_\theta$ while the Equation (14) in v2 ...
14
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1answer
2k views

Why does DQN require two different networks?

I was going through this implementation of DQN and I see that on line 124 and 125 two different Q networks have been initialized. From my understanding, I think one network predicts the appropriate ...
3
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1answer
1k views

How should I model all available actions of a chess game in deep Q-learning?

I just read about deep Q-learning, which is using a neural network for the value function instead of a table. I saw the example here: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html and ...
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2answers
437 views

What is experience replay in laymen's terms?

I've been reading Google's DeepMind Atari paper and I'm trying to understand the concept of "experience replay". Experience replay comes up in a lot of other reinforcement learning papers (...
3
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1answer
754 views

Why use semi-gradient instead of full gradient in RL problems, when using function approximation?

Semi-gradient methods work well in reinforcement learning, but what is there a reason of not using the true gradient if it can be computed? I tried it on the cart pole problem with a deep Q-network ...
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0answers
205 views

Understanding multi-iteration updates of the model in the Proximal Policy Optimization algorithm

I have a general question about the updating of the network/model in the PPO algorithm. If I understand it correctly, there are multiple iterations of weight updates done on the model with data that ...
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3answers
1k views

How can you represent the state and action spaces for a card game in the case of a variable number of cards and actions?

I know how a machine can learn to play Atari games (Breakout): Playing Atari with Reinforcement Learning. With the same technique, it is even possible to play FPS games (Doom): Playing FPS Games with ...
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2answers
575 views

Is it possible to implement reinforcement learning using a neural network?

I've implemented the reinforcement learning algorithm for an agent to play snappy bird (a shameless cheap ripoff of flappy bird) utilizing a q-table for storing the history for future lookups. It ...

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