Questions tagged [reinforcement-learning]

For questions related to reinforcement learning, i.e. a machine learning technique where we imagine an agent that interacts with an environment (composed of states) in time steps by taking actions and receiving rewards (or reinforcements), then, based on these interactions, the agent tries to find a policy (i.e. a behavioural strategy) that maximizes the cumulative reward (in the long run), so the goal of the agent is to maximize the reward.

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43 views

Can Reinforcement Learning be used for UAV waypoint control?

I want to make a drone which can follow static and dynamic waypoints. I am a total beginner in the drone field so I can't figure out that should I use Reinforcement Learning or any other learning ...
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93 views

Should the network weights converge when training Deep Q networks?

I have two sets of data, one training and one test set. I use the train set to train the deep q network model variant. I also continuously evaluate the agent Q values obtained on the test set every ...
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How should I define the loss function when using DQN to estimate the probability density?

I'm doing a Deep Q-learning project. All of my rewards are positive and there are two terminal states. One of them has a zero reward and the other has a high positive reward. The rewards are ...
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How to understand and visualize a trained RL agent's policy when the state space is high dimensional?

What are typical ways to understand and visualize a trained RL agent's policy when the state space is of high dimension (but not images)? For example, suppose state and action are denoted by $s=(...
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47 views

Understanding V- and Q-functions

Assume the existence of a Markov Decision Process consisting of: State space $S$ Action space $A$ Transition model $T: S \times A \times S \to [0,1]$ Reward function $R: S \times A \times S \to \...
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Curiosity Driven Learning affect optimal policy

I am trying to understand some of the different approaches used to overcome sparse rewards in a reinforcement learning setting for a research project. Particularly, I have looked at curiosity driven ...
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24 views

Ideas on how to train an AI to play Mario Kart with the DeSmuME Emulator

Gday guys, i have this idea in my mind for quite a while. I want to teach an AI to play Mario Kart on the NDS, which can hopefully beat me and my friends one day. Iam familiar with the theoretical ...
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72 views

DQN unlearns certain OpenAI-Gym environments

I solved the OpenAI-Gym MountainCar-v0 environment using dqn(using low-state-dimensional input). When I used the same code for solving CartPole-v0 environment, the network got trained in the reverse ...
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31 views

Are there real-world problems where case-based reasoning is not suitable?

I know case-based reasoning has four stages: retrieve, retain, re-use and revise. Used for solving new problems by adapting solutions that were used to solve old problems, like car issues. The ...
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391 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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219 views

How to back-propagate illegal actions for policy gradient learning

When training a AI RL agent to play a game there'll be situations where the AI cannot perform certain actions lest they violate the game rules. That's easy to handle, and I can set illegal actions to ...
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41 views

ADVANTAGE ACTOR CRITIC WITH TWO ACTIONS

What is the Loss Of Advantage Actor Critic in case there are two actions taken place SIMULTANEOUSLY. Is it ok to be written in this way ? please if anyone knows references addressing this matter, that ...
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35 views

Exploration for softmax should be binary or continuous softmax?

Maybe it's silly to ask but for random exploration in an RL for choosing discrete action, that in the neural network last layer softmax will be used, what random samples should we provide? binary like ...
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532 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 ...
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56 views

Why the optimal Bellman operator of a Q-function can be approximated by a single point

I am currently studying reinforcement learning, especially DQN. In DQN, learning proceeds in such a way as to minimize the norm (least-squares, Huber, etc.) of the optimal Bellman equation and the ...

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