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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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14 views

Why don't people use projected bellman error with deep nets?

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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0answers
24 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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0answers
42 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 ...
8
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2answers
82 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 regarding the learning rate are satisfied $\sum_{t} \alpha_t(s, a) = \infty$ $...
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1answer
26 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
60 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 (https://arxiv.org/pdf/...
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15 views

How to limit actions based on a state [duplicate]

I'm trying to implement DQN using tf-agents for simple environment. So far I have ...
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1answer
35 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? ...
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29 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 ...
2
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1answer
37 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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17 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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26 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
80 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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12 views

Extend Gym Environment for recommendation with kerasRL and some questions

I want to make a simple recommendation system based on reinforcement learning using kerasRL and OpenAI Gym. My point is that I'...
2
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1answer
73 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
71 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
82 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 ...
4
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2answers
148 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
99 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
50 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
46 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: ...
1
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1answer
54 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
43 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
56 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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2answers
249 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 (...
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0answers
141 views

Understanding multi iteration update of model in Policy Gradient PPO 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 ...