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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Why does reinforcement learning using a non-linear function approximator diverge when using strongly correlated data as input?

While reading the DQN paper, I found that randomly selecting and learning samples reduced divergence in RL using a non-linear function approximator (e.g a neural network). So why does Reinforcement ...
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241 views

What could be causing the drastic performance drop of the DQN model on the Pong environment?

I am running a basic DQN (Deep Q-Network) on the Pong environment. Not a CNN, just a 3 layer linear neural net with ReLUs. It seems to work for the most part, but at some point, my model suffers from ...
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71 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 ...
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33 views

How to take actions at each episode and within each step of the episode in deep Q learning?

In deep Q learning, we execute the algorithm for each episode, and for each step within an episode, we take an action and record a reward. I have a situation where my action is 2-tuple $a=(a_1,a_2)$. ...
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36 views

Understanding the TensorFlow implementation of the policy gradient method

I was trying to understand the implementation of a basic policy gradient (REINFORCE) method using TensorFlow. I think I got almost everything. The only thing that still bothers me is the loss function ...
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1answer
42 views

How can a single sample represent the expectation in gradient temporal difference learning?

I was reading the gradient temporal difference learning version 2(GTD2) from rich Sutton's book page-246. At some point, he expressed the whole expectation using a single sample from the environment. ...
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61 views

Why AlphaGo didn't use Deep Q-Learning?

In the previous research, in 2015, Deep Q-Learning shows its great performance on single player Atari Games. But why do AlphaGo's researchers use CNN + MCTS instead of Deep Q-Learning? is that because ...
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21 views

How does normalization of the inputs work in the context of PPO?

What does the normalization of the inputs mean in the context of PPO? At each time step of an episode, I only know the values of this time step and of the previous ones, if I take track of them. This ...
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17 views

How does policy evaluation work for continuous state space model-free approaches?

How does policy evaluation work for continuous state space model-free approaches? Theoretically, a model-based approach for the discrete state and action space can be computed via dynamic programming ...
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34 views

How to deal with nonstationary rewards in asymmetric self-play reinforcement learning?

Suppose we're training two agents to play an asymmetric game from scratch using self play (like Zerg vs. Protoss in Starcraft). During training one of the agents can become stronger (discover a good ...
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1answer
67 views

What could be the cause of the drop in the reward in A3C?

The mean episodic reward is generally increasing, but it has spontaneous drops, and I'm not sure of their cause. The problem has a sparse reward, batch size=2000, <...
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142 views

Deep Q-Network (DQN) to learn the game 2048

I am trying to build a Deep Q-Network (DQN) agent that can learn to play the game 2048. I am orientating myself on other programs and articles that are based on the game snake and it worked well (...
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25 views

Training a reinforcement learning model with multiple images

I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (e.g., a different "maze"). Suppose I have about $10K$ ...
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107 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 ...
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45 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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144 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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1answer
79 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 ...
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186 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 ...
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20 views

Are the final states not being updated in this $n$-step Q-Learning algorithm?

I am reading this paper and in algorithm 3 they describe an $n$-step Q-Learning algorithm. Below is the pseudo-code. From this pseudo-code, it looks as though the final tuples that they would ...
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Does the concept of validation loss apply to training deep Q networks?

In deep learning, the concept of validation loss is to ensure that the model being trained is not currently overfitting the data. Is there a similar concept of overfitting in deep q learning? Given ...
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28 views

How was the DQN trained to play many games?

Some people claim that DQN was used to play many Atari games. But what actually happened? Was DQN trained only once (with some data from all games) or was it trained separately for each game? What was ...
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43 views

How much time does it take to train DQN on Atari environment?

I am trying to build a DQN model for the Atari Pong game, but I am not sure whether the model is learning at all. I am using the architecture described in the paper Playing Atari with Deep ...
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1answer
101 views

Which deep reinforcement learning algorithm is appropriate for my problem?

My task is to solve an optimization problem with deep reinforcement learning. I read about several algorithms like DQN, PPO, DDPG, and A2C/A3C but use cases always seem to be problems like video games ...
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55 views

How to correctly implement self-play with DQN?

I have an environment where an agent faces an equal opponent, and while I've achieved OK performance implementing DQN and treating the opponent as a part of the environment, I think performance would ...
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38 views

How does adding noise to the action in DDPG help in learning?

I can't understand how playing with the action generated by the actor network in DDPG by adding the noise term helps in exploration.
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32 views

Policy $\pi$ obtained from inverse reinforcement learning vs reward shaping

I am working on a research project about the different reward functions being used in RL domain. I have read up on Inverse Reinforcement Learning (IRL) and Reward Shaping (RS). I would like to clarify ...
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50 views

Torch CNN not training

I am completely new to CNN's, and I do not quite know how to design or use them efficiently. That being said, I am attempting to build a CNN that learns to play Pac-man with reinforcement learning. I ...
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39 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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184 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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60 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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1answer
32 views

How to predict Q-values based on stack of frames

I decided to train deep Q-learning agent based on getting raw pixels from environment.I have one particular problem:when I input stack of frames, suppose 4 consecutive frames, if action space is 6,...
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59 views

How can the target rely on untrained parameters?

I'm trying to understand DQN. I understand where the loss function comes from. I'm just unsure about why the target function works in practice. Given the loss function $$ L_i(\theta_i) = [(y_i - Q(s,a;...
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32 views

How to prevent deep Q-learning algorithms to overfit?

I have recently solved the Cartpole problem using double deep Q-learning. When I saw how the agent was doing, it used to go right every time, never left, and it did similar actions all the time. Did ...
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19 views

Should we start with a small batch-size and increase during training to improve sample efficiency?

Just made an interesting observation playing around with the stable-baseline's implementation of PPO and the BipedalWalker environment from OpenAI's Gym. But I believe this should be a general ...
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1answer
29 views

Is it possible to prove that the target policy is better than the behavioural policy based on learned Q values?

I have retrospective data for a sort of "behaviour policy" which I will use to train a deep q network to learn a target greedy policy. After learning the Q values for this target policy, can we make ...
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19 views

Why is it hard to prove the convergence of the deep Q-learning algorithm?

Why is it hard to prove the convergence of the DQN algorithm? We know that the tabular Q-learning algorithm converges to the optimal Q-values, and with a linear approximator convergence is proved. ...
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24 views

State-of-the-art algorithms not working on a custom RL environment

I'm trying to train a RL agent on a custom, highly stochastic environment (MDP). In order to do so I'm using existing implementations of state-of-the-art RL algorithms as provided by Stable Baselines. ...
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21 views

When does Monte Carlo linear function approximation converge?

In this Stanford lecture (minute 35:47 and 37:00), the professor says that Monte Carlo (MC) linear function approximation does not always converge, and she gives an example. In general, when does MC ...
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17 views

Why are Dueling Q Networks not used more often to approximate Q-values in reinforcement learning algorithms?

I've just learned about Dueling Network Architectures to estimate $Q$-values and am wondering why this architecture is not used more often in deep RL algorithms? DDPG and TD3 estimate the $Q$-function ...
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18 views

Do the variance and bias belong to the policy or value functions?

Recently, I read many papers on variance and bias. But I am still confused by the two notions, the variance or bias belongs to who? Policy or value? If the variance or bias is large or low, what ...
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18 views

NaNs after a while in training of PPO

My problem is that every time I am trying to train my PPO agent I get NaN values after a while. The diagnostic that I get is the following: ...
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1answer
107 views

Understanding the loss function in deep Q-learning

I am trying to understand how deep Q learning (DQN) works. To my current understanding, each $Q(s, a)$ functions is estimated to be a function of a feature vector of its state $\phi$(s) and the weight ...
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21 views

Hyperparameter optimisation over entire range or shorter range of training episodes in Deep Reinforcement Learning

I am optimising hyperparameters for my deep reinforcement learning project (using PPO2, DQN and A2C) and was wondering: Should I find the optimum hyperparameters to get maximum reward from training ...
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32 views

Is the training of multi-version of the same system at the same time affecting the results?

I'm using DQN to train multi-version of the same system and there is a small difference when I run them both separately. However, my result suddenly dropped in both versions if I run them both at the ...
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36 views

Questions performance SimPLe pong for AI demo

For a demo I need to develop an AI solution to learn how to play pong. I have the following requirements: Computer needs to play against a human player. Learn while playing the game. Poor AI result ...
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46 views

Can next state and action be same in Deep Deterministic Policy Gradient?

I am trying to apply deep deterministic policy gradient (DDPG) on a robotic application. My states consist of the joint angle positions of the robot and my actions are also its joint angle positions. ...
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177 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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28 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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36 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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20 views

How to implement RAM versions of Atari games

I have coded the breakout RAM version, but, unfortunately, its highest reward was 5. I trained it for about 2 hours and never reached a higher score. The code is huge, so I can't paste here, but, in ...