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Questions tagged [open-ai]

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

30 questions with no upvoted or accepted answers
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182 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 ...
3
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0answers
42 views

Is it possible to insert a new map in the OpenAI Gym Taxi V.2?

I would like to train a neural network with the OpenAI Gym Taxi and see how it would react on a new map. Is it possible to insert a new map in the OpenAI Gym Taxi V.2?
2
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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 ...
2
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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....
2
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0answers
82 views

What is a high performing network architecture to use in a PPO2 MlpLnLstmPolicy RL model?

I am playing around with creating custom architectures in stable-baselines. Specifically I am training an agent using a PPO2 model. My question is, are there some rules of thumb or best practices in ...
2
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0answers
419 views

How many episodes does it take for a vanilla one-step actor-critic agent to master the OpenAI BipedalWalker-v2 problem?

I'm trying to solve the OpenAI BipedalWalker-v2 by using a one-step actor-critic agent. I'm implementing the solution using python and tensorflow. I'm following this pseudo-code taken from the book ...
1
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0answers
42 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 ...
1
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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 ...
1
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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 ...
1
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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 ...
1
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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 ...
1
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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. ...
1
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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
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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
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0answers
111 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
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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 ...
1
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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
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0answers
171 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
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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 ...
1
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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 ...
1
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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
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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 ...
1
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0answers
131 views

Understanding policy update in PPO2

I have a question regarding the functionality of the PPO2 algorithm together with the Stable Baselines implementation: From the original paper I know that the policy parameters $\theta$ are updated K-...
1
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2answers
102 views

Running 2 NEAT nets on the same observations

So i have been playing around with neat-python. I made a program, applying neat, to play pinball on the Atari 2600. The code for that can be found in the file ...
0
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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 ...
0
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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
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0answers
35 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 ...
0
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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 ...
0
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0answers
83 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 ...
0
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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) ...