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Questions tagged [openai-gym]

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Gym Cartpole not solving with Cross Entropy Method?

Cross Entropy Method is considered as one of the simplest optimization algorithm which can be used for training an agent. I tried to train an agent to solve gym's cartpole environment and I have used ...
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6 views

Why does my Deep Q Model only take a single action?

I don't know if this is the proper place to ask code-based questions on but I've been struggling with this issue for a while. Basically I am training a Deep Q Model using Keras and Google Colab (for ...
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1answer
94 views

Valid actions in OpenAI Gym

Why don't the gym environments come with "valid actions"? The normal gym environment accepts as input any action, even if it's not even possible. Is this a normal thing in reinforcement learning? Do ...
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What are the main reasons which does not cause the training error of yolov2 to not diminish?

I am using https://github.com/thtrieu/darkflow yolov2 for detecting and classifying the images of passport. There are 8 classes and all the objects are passport details like name, father name, mother ...
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1answer
18 views

Do RL agents learn the optimal “degree” of an action to take?

I have a game environment I want to train an RL model on. This environment has 2 fundamental actions that the agent can take; "Left" or "Right" (say, 0 or 1). However, the actions "Left" or "Right" ...
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1answer
45 views

Does a larger action space take longer to train an RL agent?

I am playing around with the openai gym to try and better understand reinforcement learning. One agent parameter you can modify is the action space i.e. the specific actions an agent can take in an ...
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18 views

How to select initializers for DDQN in Keras?

I'm using DDQN for OpenAI Gym games (like CartPole, MountainCar). It occurred to me that the weight/bias initialization might have a reasonable impact on how quickly the network starts to learn so I ...
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1answer
145 views

Why could my DDQN get significantly worse after beating the game repeatedly?

I've been trying to train a DDQN to play OpenAI Gym's CartPole-v1, but found that although it starts off well and starts getting full score (500) repeatedly (at around 600 episodes in the pic below), ...
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1answer
395 views

openai gym - what is an agent I can use with a multi-discrete action space?

I have a custom environment with a multi-discrete action space. The action and observation spaces are as follows: Action: ...
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1answer
135 views

A2C Continuous for Pendulum-v0 working implementation, negation for loss and entropy calculation

very good implementation of A2C continuous for Pendulum-v0 Code has snippet to stop execution when mean of last 10 or 20 is higher than -20 but the results look like: ...
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63 views

CartPole v1 - Simple backprop with 1 hidden layer

I'm trying to solve the CartPole-v1 problem from OpenAI by using backprop on a one-layer neural network - while updating the model at every time step using State action values (Q(s,a)). I'm unable to ...
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73 views

Reinforcement Learning using PPO2 in openai gym retro, mario not learning the clear the easy episode

I am training mario game in retro using ppo2 baselines for some time. I have tried level3 and level1 too. But even after full training when I play using saved checkpoints, the mario is not able to ...
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22 views

Fetch (multi joint robot) DQN training: How to do action selection?

I am implementing a DQN using a similar environment to OpenAI fetch envs. I am trying to convert them to pybullet implementations. When training a DQN for a multi-joint robot like the Fetch, ...
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1answer
111 views

How does DQN solve Open AI Cartpole - v0?

Context I am confused about how a DQN is supposed to solve the cart pole problem since the rewards are so dense. I have been using pytorch example. I am aware of some solutions, but I have issue with ...
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1k views

PPO, A2C for continuous action spaces, math and code

Edit: Question has been edited to better reflect what I learned after asking the original question. I implemented the clipped objective PPO-clip as explained here: https://spinningup.openai.com/en/...
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1answer
58 views

Openai Spaces for a modified environment

I have a 2-dimensional array of normalized data. I am using space = np.array([0,1,...366],[0,0.000001,.....1]) I need to fit this as an observation space in ...
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2answers
154 views

Gym action space for board game with reward function

Im trying to design an openai gym environment that plays a quite simple board game where each player has 16 pieces that are exactly the same in regard to how they can move. The board is 10x10 and ...
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1answer
63 views

How to represent an image as state in a Q-table

I'm trying to do Q-learning with the Atari games using the gym python's package. I want to use the image as the state of my algorithm, but I came up with a doubt: ...
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106 views

comparison of linear Q-learning and DQN

I saw in DQN nature paper 2015 https://www.nature.com/articles/nature14236(Extended Data Table 4) some comparisons between DQN and linear Q-learning. The ratio ...
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1answer
146 views

What is wrong with this reinforcement learning environment ?

I'm working on below reinforcement learning problem: I have bottle of fix capacity (say 5 liters). At the bottom of bottle there is cock to remove water. The distribution of removal of water is not ...
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1answer
33 views

Why the invariant reward helps training?

I am new to Machine Learning, and I am trying to solve MountainCar-v0 using Q-learning. I can solve the problem now, but I am still confused. According to the MountainCar-v0's Wiki, the reward ...
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1answer
356 views

Card game for Gym: Reward shaping

I am working on a card game for openai gym and currently I ask myself how to shape the reward function for it. One round of the game consists of each player picking a card from its hand, whereas not ...
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1answer
1k views

What is a minimal setup to solve the CartPole-v0 with DQN?

I solved the CartPole-v0 with a CEM agent pretty easily (experiments and code), but I struggle to find a setup which works with DQN. Do you know which parameters should be adjusted so that the mean ...