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Questions tagged [environment]

For questions related to the concept of environment in reinforcement learning and other AI sub-fields.

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3
votes
2answers
38 views

Perfect Information and Imperfect Information Games

In perfect information games, the agent can see all the moves performed in the past. Besides, it can observe the next action that will be put into practice by the opponent. In this case, can we say ...
4
votes
1answer
54 views

How to assign rewards in a non-Markovian environment?

I am quite new to the Reinforcement Learning domain and I am curious about something. It seems to be the case that the majority of current research assumes Markovian environments, that is, future ...
2
votes
1answer
49 views

Can an RL algorithm trained in one environment be successful in a different one?

Can an RL algorithm trained in one environment be successful in a different one? For example, if I train a model to go through one labyrinth, could this model also go through a different but similar ...
4
votes
1answer
72 views

Benchmarks for reinforcement learning in discrete MDPs

To compare the performance of various algorithms for perfect information games, reasonable benchmarks include reversi and m,n,k-games (generalized tic-tac-toe). For imperfect information games, ...
2
votes
1answer
32 views

How to represent players in a multi agent environment so each model can distinguish its own player

So I have 2 models trained with DQN algorithm that I want to train in a multi-agent environment to see how they react with each other. The models were trained in an environment consisting of 0's and 1'...
2
votes
2answers
41 views

Can non-Markov environments also be deterministic?

The definition of deterministic environment I am familiar with goes as follows: The next state of the agent depends only on the current state and the action chosen by the agent. By exclusion, ...
1
vote
0answers
26 views

How do I know if the assumption of a static environment is made?

An important property of a reinforcement learning problem is whether the environment of the agent is static, which means that nothing changes if the agent remains inactive. Different learning methods ...
2
votes
1answer
483 views

How to create a custom environment for reinforcement learning

I am a newbie in reinforcement learning working on a college project. The project is related to optimizing the hardware power. I am running proprietary software in Linux distribution (16.04). The goal ...
0
votes
1answer
112 views

Advice on creating a new environment using OpenAI Gym

I'm looking for some general advice here before I dive in. I'm interested in creating a new environment for OpenAI gym to provide some slightly more challenging continuous control problems than the ...
1
vote
0answers
18 views

What are a list of board game environments for RL practice?

Recently OpenAI removes their board game environments. (It may be possible to install an older version to get access to them, but I haven’t downgraded). Is there a list of repositories or resources ...
1
vote
0answers
74 views

Difficulty in balancing Pendulum using Deep Reinforcement Learning Algorithm

I am using OpenAI Gym framework for reinforcement learning where I am trying solve classic control problem of balancing an Inverted Pendulum, which is similar to the "Pendulum-v0" with some changes in ...
2
votes
1answer
121 views

Is there a way to train an RL agent without any environment?

Following Deep Q-learning from Demonstrations, I'd like to avoid potentially unsafe behavior during early learning by making use of supervised learning with demonstration data. However, the ...
0
votes
1answer
156 views

What is the use of the seed function in the gym Environment 'Pendulum-v0'?

Why do we use the seed function in the 'Pendulum-v0' environment? https://github.com/openai/gym/blob/master/gym/envs/classic_control/pendulum.py#L25
10
votes
4answers
976 views

Is the optimal policy always stochastic if the environment is also stochastic?

Is the optimal policy always stochastic (that is, a map from states to a probability distribution over actions) if the environment is also stochastic? Intuitively, if the environment is ...
5
votes
2answers
600 views

How does Q-learning work in stochastic environments?

The Q function uses the (current and future) states to determine the action that gets the highest reward. However, in a stochastic environment, the current action (at the current state) does not ...
4
votes
1answer
58 views

Are all fully observable environments episodic?

According to the definition of a fully observable environment in Russell & Norvig, AIMA (2nd ed), pages 41-44, an environment is only fully observable if it requires zero memory for an agent to ...
5
votes
2answers
378 views

How can a neural network work with continuous time?

I have an ANN model that receives an input and produces an output. The output is an action that interacts with the environment and changes the input accordingly. The network has a desired environment ...