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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How to choose hyperparameters in double DQN?

I'm looking for some indications about the tuning of hyper-parameters in building my double DQN. I have a time series problem (with about 2000 observations and no terminal state, I have to max the ...
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Is there any advantage to using a non-diagonal covariance matrix for a policy distribution?

For reinforcement learning implementations with a gym.spaces.Box action space, which is the product of $k$ real closed intervals, it is common (actually more like ...
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Why aren’t heuristics for Connect Four Monte Carlo tree search improving the agent?

I’ve created an agent using MCTS to play Connect Four. It wins against humans pretty well, but I’d like to improve upon it. I decided to add domain knowledge to the MCTS rollout stage. My evaluation ...
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How is the parameterised server updated in distributed DQN?

In this paper about Massively Parallel Methods for Deep Reinforcement Learning, the parallelisation of DQN is done via separating the actors and learners. Multiple actors carry out the $\epsilon$ ...
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How to observe or measure convergence of Monte Carlo Tree Search?

As above: how does one observe/measure a Monte Carlo Tree Search to be able to update the algorithm and compare results?
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143 views

OpenAI Gym: Multiple actions in one step

I'm trying to design an OpenAI Gym environment in which multiple users/players perform actions over time. It's round based and each user needs to take an action before the round is evaluated and the ...
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When has RL helped in building Recommender Systems?

I was wondering if it'd be possible to list some or all the instances wherein Reinforcement Learning has been used to build Recommender Systems - here's one paper I've already come across, and I found ...
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54 views

Reinforcement learning random agent always performing the same few actions

I have a DQN model which has 3 features as inputs (properly normalized) and should output q-values for each of the 100 possible actions. However, prior to any training, I would like to examine the ...
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57 views

Why is diversity of reasoning paths important in recommender systems using knowledge graphs?

This is a continuation of the discussion that originates on this StackExchange post, about recommender systems using knowledge graphs(KGs). For those who might not prefer reading the original post, I ...
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Won't the copy of the weights of the worker model to the global model erase the work of other workers in A3C?

I was reading the article Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor-Critic (A3C) with tf.keras and eager execution. From my understanding, we copy the weights ...
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In RL can you use a random sample from the state as observation for the agent?

Could reinforcement learning work in the following context : Given that the initial state space is very large (10^6) and the actions would only effect a subspace of the state could we randomly select ...
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Why would you ignore episodes that loop back on the starting state in MCTS?

After reading about MCTS for policy learning and optimization, I don't understand why you would want to ignore episodes that loop back on the starting state. What advantage does this have and why ...
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DQN is unable to learn from image data

I am trying to write a DQN model that will be able to solve OpenAI gym CartPole environment. I successfully managed to do it using scalar observation data that ...
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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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36 views

Non-Neural Network algorithms for large state space in zero sum games

I was reading online that Tic Tac Toe has a state space of 3^9 = 19,683. From my basic understanding, this sounds too large to use with Q Learning, as the Q table would be huge? If that is the case, ...
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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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Stable baseline learn from online game

i created a custom environment for an online game. i want to know how can i train my model with stable baseline because learn function in stable baseline just take number of steps and in online game ...
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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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Which hidden state should I use for a trajectory when incorporating LSTM into RL?

I'm trying to wrap my head around using LSTM in an RL algorithm like actor-critic or PPO. I've found this Github code which presents this in a very simple manner, however I have a very limited ...
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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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Concrete examples of models and policies in Tic Tac Toe environment

I'm having difficulty picturing how models and policies are represented. Could someone describe how they would look in the context/environment of a game of Tic Tac Toe? For example, "In Tic Tac Toe, ...
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How to pass observation from CartPole-v0 to neural network using tensorflow

I am trying to learn about RL by implementing DQN with tensorflow. However, I am really stuck with tensorflow.. I just don't understand it. I think I have found the core of what I understand - I dont ...
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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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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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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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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? ...
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105 views

Having trouble solving Pong. My model is not improving

Im trying to solve pong by a DQN approach. These are my hyper parameters: ...
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177 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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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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