Questions tagged [open-ai]

For questions related to the openAI, including the Gym toolkit.

Filter by
Sorted by
Tagged with
1
vote
1answer
34 views

Delayed state observation or caching action in OpenAI gym. Can it still learn?

I am planning to use OpenAI gym for my experiment in real life. In my experiment design, by the limits of a real-life scenario, I can only receive the state information or the rewards about 2-3 ...
2
votes
1answer
28 views

Why adding a baseline doesn't affect the policy gradient?

On the OpenAI's Spinning Up, they justify the fact that adding a baseline $b(s_t)$ in the policy gradient doesn't change its gradient by saying that this is an immediate consequence of the EGLP Lemma ...
1
vote
2answers
130 views

Will AI replace software developers?

I'm 26, working for five years as a software developer and really worried because of new technologies like Github Copilot and GPT-3 in general. Do you think AI will replace software developers? If so, ...
1
vote
0answers
41 views

Too slow search using MCTS in OpenAI Atari games

I'm recently using Monte Carlo Tree Search in OpenAi Gym Atari, but the result isn't satisfying. Without render, the game lasts about 180 steps ( env.step() was called this much time ) with random ...
0
votes
0answers
33 views

Bridging the gap between simulation and real-world scenarios!

I've got a DRL model that was trained on a simulation at a frame rate of 100fps, after testing it with 100fps it gives good results however when testing it with another frame rate say 50fps it gives a ...
1
vote
1answer
42 views

Why does the Atari Gym Amidar environment only move after a certain number of episodes? [closed]

When I try to run Amidar even without RL code, I cannot get the environment to move immediately. It takes about 100 steps before the game actually starts moving. I use the following simple code to ...
0
votes
0answers
51 views

Open AI Taxi - Agent fails to learn an effective policy

I'm trying to solve the openai gym taxi problem (v3) using deep q learning. I've already had some success with the q-table approach, but for the life of me cannot manage to train a NN to learn a ...
0
votes
1answer
33 views

Is is not possible to achieve average reward of more than 20-40 with simple Q-Learning

I have implemented the simple Q-Learning based solution for AI-gym's Cartpole-v0. However, despite changing hyper-parameters, and rechecking my code, I cannot get an average reward (N-running reward) ...
1
vote
0answers
25 views

What is the meaning of "Our current objective weights every token equally and lacks a notion of what is most important to predict" in the GPT-3 paper?

On page 34 of OpenAI's GPT-3, there is a sentence demonstrating the limitation of objective function: Our current objective weights every token equally and lacks a notion of what is most important to ...
0
votes
0answers
34 views

Deployment of a DeepRL model trained on a custom OpenAI-GYM environment

I developed a custom OpenAI-GYM environment and trained a CDQN model on it, now I am trying to figure out how can I test it not using my gym environment but in production (using real world ...
1
vote
0answers
54 views

How should I simulate this Markov Decision Process?

I am working on solving a problem on nodes in a graph communicating with each other. They try to estimate a central state using Kalman consensus filter, with the connections described by the graph's ...
0
votes
2answers
292 views

Simple DQN too slow to train [closed]

I have been trying to solve the OpenAI lunar lander game with a DQN taken from this paper https://arxiv.org/pdf/2006.04938v2.pdf The issue is that it takes 12 hours to train 50 episodes so something ...
1
vote
0answers
102 views

How to deal with KerasRL DDPG algorithm getting stuck in a local optima?

I am using KerasRL DDPG to try to learn a policy on my own custom environment, but the agent is stuck in a local optima although I am adding the OrnsteinUhlenbeck randomization process. I used the ...
0
votes
0answers
26 views

Is there a resource that explains which settings mean 'High' or 'Low' difficulty in the ALE environment?

I have been using AIgym to train my RL agents. I am now trying to take advantage of the different difficulty settings that the ALE offers. However I can't find a resource that explains which ...
1
vote
1answer
225 views

How do I create a custom gym environment based on an image?

I am trying to create my own gym environment for the A3C algorithm (one implementation is here). The custom environment is a simple login form for any site. I want to create an environment from an ...
1
vote
0answers
31 views

What is the difference between step_model and train_model in the OpenAI implementation of the A2C algorithm?

I'm struggling a little with understanding the OpenAI implementation of A2C in the baselines (version 2.9.0) package. From my understanding, one ...
5
votes
2answers
288 views

My Deep Q-Learning Network does not learn for OpenAI gym's cartpole problem

I am implementing OpenAI gym's cartpole problem using Deep Q-Learning (DQN). I followed tutorials (video and otherwise) and learned all about it. I implemented a code for myself and I thought it ...
2
votes
0answers
70 views

Most of state-action pairs remain unvisited in the q-table

In building my first Q-learning algorithm for OpenAI gym's CartPole problem, many of my states remain unvisited. I believe it is the reason that my agent does not learn. Can I be told of the reasons I ...
1
vote
0answers
85 views

OpenAI gym's CartPole problem system does not learn

My OpenAI CartPole-v0 problem's implementation using basic Q-learning does not learn at all. I am a beginner and have implemented my first ever Q-learning from scratch after learning from tutorials. ...
5
votes
1answer
2k views

What exactly are the "parameters" in GPT-3's 175 billion parameters and how are they chosen/generated?

When I studied neural networks, parameters were learning rate, batch size etc. But even GPT3's ArXiv paper does not mention anything about what exactly the parameters are, but gives a small hint that ...
4
votes
1answer
229 views

Why is GPT-3 such a game changer?

I've been hearing a lot about GPT-3 by OpenAI, and that it's a simple to use API with text in text out and has a big neural network off 175B parameters. But how did they achieve this huge number of ...
0
votes
1answer
318 views

How can I change observation states' values in OpenAI gym's cartpole environment?

I am learning with the OpenAI gym's cart pole environment. I want to make the observation states discrete (with small stepsize) and for that purpose, I need to change two of the observations from [$ -\...
1
vote
0answers
40 views

How can I convert a simple CLI RPG to a compatible environment for training an RL agent via stable-baselines?

What would be the good choice of algorithm to use for character action selection in an RPG, implemented in Python? I had previously asked this question in the hopes of getting headway on the AI ...
1
vote
1answer
265 views

How do I test an LSTM-based reinforcement learning model using any Atari games in OpenAI gym?

I am writing a couple of different reinforcement learning models based on Rainbow DQN or some PG models. All of them internally use an LSTM network because my project is using time series data. I ...
1
vote
1answer
72 views

Is there a general file type associated with AI projects?

This is a general question. Is there a general file type associated with AI projects? Photoshop = .psd Excel = csv Artificial Intelligence = ?
0
votes
1answer
39 views

How to add more than 1 agent in one generation with Q Learning

Sometimes the agent learns a bit slow and you want to have multiple agents in one generation. And at each episode you'll draw on the screen only the best of them or all of them. How is that possible? ...
2
votes
1answer
98 views

What should the action space for the card game Crib be?

I'm working on creating an environment for a card game, which the agent chooses to discard certain cards in the first phase of the game, and uses the remaining cards to play with. (The game is Crib if ...
1
vote
1answer
120 views

Can we combine Off-Policy with On-Policy Algorithms?

On-Policy Algorithms like PPO directly maximize the performance objective or an approximation of it. They tend to be quite stable and reliable but are often sample inefficient. Off-Policy Algorithms ...
1
vote
0answers
40 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. ...
1
vote
1answer
577 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 ...
1
vote
0answers
110 views

How do i start building an autoclick bot for pubg mobile?

I want to make a bot which clicks the fire button on the mobile screen upon seeing an enemies head. In pubg mobile which is an android game you have to control the fire button and the aim along with ...
1
vote
0answers
137 views

How to integrate dict space of OpenAI gym into a reinforcement learning framework?

I am implementing a gym environment and I have several input arrays as my input (different sizes). The most simple method to integrate my environment into the gym is to use the dict space as my ...
2
votes
0answers
167 views

How to formulate normalization/probability conditions on state-action spaces in Gym?

I intend to develop a custom environment for open-ai's gym. My goal is for an agent to learn (among additional objectives) dividing a certain quantity drawn from a continous action space (i.e. spaces....
1
vote
0answers
74 views

How to define observation and action space for an array-like input?

I am working on a problem, and I want to implement it as a reinforcement learning problem and integrate it into the OpenAI's gym. My states are in the form of lists of length $n$, where each element ...
1
vote
0answers
170 views

How can I add logic for invalid moves when using stable-baselines in OpenAI's gym?

I want to integrate my environment into the OpenAI's gym and then use the stable baselines library for training it. The learning method in the stable baseline is with one-line learning and you don't ...
1
vote
0answers
33 views

How should I avoid illegal states in OpenAI's gym?

I'm trying to make a gym environment for a simulation problem. In my gym environment, I have a set of illegal states which I don't want my agent to go into them. What is the easiest way to add such ...
2
votes
0answers
93 views

Pre-trained Models for Topic Modelling Transfer Learning (LDA) [closed]

I've been searching online - and so far, I've been unable to find any publicly-accessible pre-trained models that can be used for LDA Topic Modeling - Transfer Learning. Can anyone share any resources ...
1
vote
1answer
294 views

OpenAI spinning up convolutional networks with PPO [closed]

I am using pytorch version of PPO and I have image input that I need to process with convolutional neural networks, are there any examples on how to set up the network? I know that stable baselines ...
4
votes
1answer
413 views

How to define an action space when an agent can take multiple sub-actions in a step?

I'm attempting to design an action space in OpenAI's gym and hitting the following roadblock. I've looked at this post which is closely related but subtly different. The environment I'm writing needs ...
4
votes
1answer
47 views

Is a neural network able to optimize itself for speed?

I am experimenting with OpenAI Gym and reinforcement learning. As far as I understood, the environment is waiting for the agent to make a decision, so it's a sequential operation like this: ...
2
votes
2answers
259 views

Simulating successful trajectories in Montezuma's Revenge turns out to be unsuccessful

I have written code in OpenAI's gym to simulate a random playing in Montezuma's Revenge where the agent randomly samples actions from the action space and tries to play the game. A success for me is ...
5
votes
1answer
943 views

How powerful is OpenAI's Gym and Universe in board games area?

I'm a big fan of computer board games and would like to make Python chess/go/shogi/mancala programs. Having heard of reinforcement learning, I decided to look at OpenAI Gym. But first of all, I would ...
0
votes
1answer
46 views

Why is this deep Q agent constantly learning just one action?

I'm trying to implement deep q learning in the OpenAI's gym "Taxi-v3" environment. But my agent only learns to do one action in every state. What am I doing wrong? Here is the Github repository with ...
1
vote
1answer
353 views

Are there OpenAI Gym continuing environments (other than inverted pendulum) and baselines?

I would like to use OpenAI Gym to solve a continuing environment, that is, a problem with a single, never-ending episode (please note I don't mean a continuous environment with continuous state and ...
1
vote
0answers
37 views

Same implementation, but agent is not learning in Retro Pong Environment

I tried to implement the exact same python coding by Andrej Karpathy to train RL agent to play Pong, except that I migrated the environment from Gym to Retro. Everything is the same except the action ...
3
votes
1answer
264 views

Can OpenAI simulations be used in real world applications?

I know that classical control systems have been used to solve the problem of the inverted pendulum - inverted pendulum. But I've seen that people have also used machine learning techniques to solve ...
0
votes
1answer
67 views

Do algorithms like OpenAI's "think up strategies"?

I was discussing with a friend whether current AI does anything remotely similar to 'thinking' and he argued that AIs that play games must think up strategies. While thinking may not be precisely ...
1
vote
0answers
26 views

How am I getting same results 30 times faster than in original HER paper?

I am reproducing the results from Hindsight Experience Replay by Andrychowicz et. al. In the original paper they present the results below, where the agent is trained for 200 epochs. 200 epochs * 800 ...
1
vote
0answers
79 views

Getting score values from openai gym rom

I am using the SpaceInvaders-ram-v0 from OpenAI gym. I want to extract the game's current score using the RAM values. How do I get it? I tried doing some research ...
0
votes
0answers
82 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 ...