Questions tagged [q-learning]

For questions related to the Q-learning algorithm, which is a model-free and temporal-difference reinforcement learning algorithm that attempts to approximate the Q function, which is a function that, given a state s and an action a, returns a real number that represents the return (or value) of state s when action a is taken from s. Q-learning was introduced in the PhD thesis "Learning from Delayed Rewards" (1989) by Watkins.

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Inconsistent definitions of the retrace

In Section 4.3 of paper Learning by Playing - Solving Sparse Reward Tasks from Scratch, the authors define Retrace as $$ Q^{ret}=\sum_{j=i}^\infty\left(\gamma^{j-i}\prod_{k=i}^jc_k\right)[r(s_j,a_j)+\...
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81 views

How can I use Q-learning for inventory decision making?

I am trying to model operational decisions in inventory control. The control policy is base stock with a fixed stock level of $S$. That is replenishment orders are placed for every demand arrival to ...
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2answers
74 views

What is the difference between return and expected return?

At a time step $t$, for a state $S_{t}$, the return is defined as the discounted cumulative reward from that time step $t$. If an agent is following a policy (which in itself is a probability ...
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42 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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1answer
57 views

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

TD3 is inspired from both double Q-learning and double DQN. In double Q-learning, I understand that Q1 and Q2 are independent because they are trained on different samples. In double DQN, I understand ...
3
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1answer
2k views

Training AI to play NES/SNES games on NN python

I am currently getting into Deep Learning and would like to set up an environment for training an Artificial Neural Network or NEAT to play simple video games on NES (Mario etc.) and SNES ( Donkey ...
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0answers
34 views

Q-learning: How to include a terminal state in updating rule?

I use Q-learning in order to determine the optimal path of an agent. I know in advance that my path is composed of exactly 3 states (so after 3 states I reach a terminal state). I would like to know ...
2
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0answers
25 views

How would one develop an action space for a game that is proprietary?

I'm currently trying to develop an RL that will teach itself to play the popular fighting game "Tekken 7". I initially had the idea of teaching it to play generally- against actual opponents with ...
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0answers
75 views

How does Friend-or-Foe Q-learning intuitively work?

I read about Q-Learning and was reading about multi-agent environments. I tried to read the paper Friend-or-Foe Q-learning, but could not understand anything, except for a very vague idea. What does ...
2
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1answer
101 views

Deep Reinforcement Learning: Rewards suddenly dip down

I am working on a deep reinforcement learning problem. The policy network has the same architecture as the one Deepmind published in 'Playing Atari with Deep Reinforcement Learning'. I am also using ...
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0answers
32 views

Hindsight Experience Replay with multiple goals

What if there are multiple goals? For example, let's consider Bit-flipping environment as described in the paper HER with one small change: Now, goal is not some specific configuration, but let's say ...
2
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1answer
281 views

Q-learning in Python

I'm working on a q-learning project that involves a "robot" solving a maze, and there is a problem with how I update the Q values (every time the robot ends up switching between two squares instead of ...
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65 views

Expected SARSA, SARSA and Q-learning

I would much appreciate if you could point me in the right direction regarding this question about targets for approximate ...
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0answers
14 views

N-tuple based tic tac toe diverges in temporal difference learning

I have n-tuple based tic tac toe. I already have perfect minimax player and perfectly trained table-based player. My n-tuple network consists of 8 different rows of 3 of the board as triplets having ...
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45 views

Reinforcement learning for a 2D game involving two players

I'd like to create an AI for a 2D game involving two players fighting against each other. The map look something like this (The map is a NxN array somehow randomly generated): Basically the players ...
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0answers
38 views

Why isn't this deep Q network agent for the snake game learning?

I wanted to combine this snake game with this DQN implementation I found in this article. First, I tried to change the NN's input layer to a 400 input. The game has a field of 20 times 20, so I ...
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0answers
25 views

What is the complexity of policy gradient algorithms compared to discrete action space algorithms?

I am using a policy gradient algorithm (actor-critic) for wireless networks. The policy gradient-based algorithm helps because it considers continuous action space. But how much does a policy ...
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37 views

Did I understand deep Q leaning right? (Implementation)

Gday guys, so I tried to implement my own enviroment and agent in order to fully understand DQNs. The enviroment is a dungeon with five states. actionspace = 2 statespace = 5 !!!Action a0 is ...
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0answers
13 views

TD losses are descreasing, but also rewards are decreasing, increasing sigma

I'm using Q-learning with some extensions such as noisy linear layers, n-steps and double DQN. The training, however, isn't that successful, my rewards are descreasing over time after a steep ...
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0answers
58 views

How is q-learning related to game trees?

At a first look, q-learning is a revolutionary strategy in realizing Artificial Intelligence. It has to do with finding a policy, a reward structure, neural networks for storing the q-table and a ...
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0answers
38 views

Unique game problem (ML, DP, PP etc)

Looking for a solution to my below game problem. I believe it to require some sort of reinforcement learning, dynamic programming, or probabilistic programming solution, but am unsure... This is my ...
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0answers
33 views

Can multiple reinforcement algorithms be applied to the same system?

Can a system, for instance robotic vehicle, be controlled by more than one reinforcement learning algorithm. I intend to use one to address collision avoidance whereas the other to tackle autonomous ...
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0answers
28 views

High variance in performance of q-learning agents trained with same parameters

I am training an agent to play a simple game using double deep q learning. However, the variance in agent performance is very high, even for agents trained with same model parameters. For example, I ...
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0answers
24 views

Measure grid-world environments difference for reinforcement learning

I'd like to measure the difference between 2 grid-worlds to determine the generalization capacity of my agent using tabular Q-learning. Example (OpenAI Frozen Lake) : SFFF FHFH FFFH HFFG and : ...
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54 views

Do we need to reset the DQN network after every episode?

I was going through this implementation of Reinforcement learning where model is being trained to manage the number of bikes at a station. Here, line 78 represents the loop over all episodes (if I ...
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0answers
61 views

Reinforcement Learning with limited number of episodes

I try to implement RL to a case something like this: This game consist of several rounds. Every round the players need to generate a maze that consists of rooms. There are around 1000 different ...
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0answers
33 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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41 views

Exploration rate decay and training in Q learning

I'm trying to replicate the results of the DeepMind's paper with Breakout included in OpenAI Gym. I wonder how much frames should I keep until I reach the fixed exploration rate. Actually it reaches ...
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0answers
80 views

Why does Q-learning converges to optimal policy even if I am acting suboptimally?

In Q-learning, during training, it doesn’t matter how I select actions. The algorithm always converges to optimal optimal policy. Why does this happen?
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1answer
112 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 ...
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0answers
52 views

Deep Q-Network concepts and implementation

How does sequential DQN work? How would one construct the simple sequential DQN? OpenAI Baselines: DQN
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150 views

Training RL agent on timeseries trading data with Continous Deep Q or NAF

I am writing an MDP based agent that is supposed to learn to place bids and asks in a trading environment. The system requests 2 values (mWh energy and $, both being positive or negative). Every ...
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0answers
151 views

Help with implementing Q-learning for a feedfoward network playing a video game

I want to train a feedforward neural network to play a video game called Puyo Puyo 2, using reinforcement learning. More specifically, I'm trying Q-learning but I'm open to better alternatives. In ...
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53 views

Why epsilon-greedy hyperparameter is annealed smoothly?

Regarding of DQN, or DQRNN, (reinforcement learning) To me, RL is a process that can be divided into 2 stages: Exploring wide range of paths (acting randomly) Refining the current optimal paths (...
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18 views

Problems while training a DQN Agent on DSTC dataset

I am trying to create a dialogue policy model on DSTC data. This model takes in a state of the conversation and outputs an act the machine must take. I am creating this model using reinforcement ...
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0answers
164 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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0answers
18 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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24 views

Is an indirect policy superior to a normal one?

Before a robot can act with meaning, some planning is needed. The idea is, that the decision making process is independent from action. The task of figuring out what the best decision is, was ...
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1answer
63 views

Model-based Reinforcement Learning algorithm for real-time robotics task

I'm quite a newbie when it comes to practically working with Deep Learning techniques, although I studied them quite a lot theoretically in the last months. However, now I'm facing my first practical ...
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1answer
42 views

Q-Learning Algorithmus does not work

Hey I am training an initialized Neural Network with this Method ...
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1answer
172 views

Problem over DQN Algorithm not converging on snake

I'm using a DQN Algorithm to play Snake. The input of the neural network is a stack of 4 images taken from the games 80x80. The output is an array of 4 values, one for every direction. The problem ...