Questions tagged [action-spaces]

For questions about action spaces in the context of reinforcement learning and other AI sub-fields.

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17
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2answers
8k views

Any papers regarding different/inconsistent action space in Reinforcement Learning? [closed]

This question is regarding Reinforcement Learning and different/inconsistent action space for every/some states. What do I mean by different/inconsistent action space? Let say you have an MDP where ...
14
votes
3answers
5k views

How to implement a variable action space in Proximal Policy Optimization?

I'm coding a Proximal Policy Optimization (PPO) agent with the Tensorforce library (which is built on top of TensorFlow). The first environment was very simple. Now, I'm diving into a more complex ...
14
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1answer
4k views

How to deal with a huge action space, where, at every step, there is a variable number of legal actions?

I am working on creating an RL-based AI for a certain board game. Just as a general overview of the game so that you understand what it's all about: It's a discrete turn-based game with a board of ...
9
votes
3answers
15k views

What do the different actions of the OpenAI gym's environment of 'Pong-v0' represent? [closed]

Printing action_space for Pong-v0 gives Discrete(6) as output, i.e. $0, 1, 2, 3, 4, 5$ are actions defined in the environment as ...
7
votes
0answers
898 views

Is there a difference in the architecture of deep reinforcement learning when multiple actions are performed instead of a single action?

I've built a deep deterministic policy gradient reinforcement learning agent to be able to handle any games/tasks that have only one action. However, the agent seems to fail horribly when there are ...
6
votes
1answer
134 views

Are there RL techniques to deal with incremental action spaces?

Let's say we have a problem that can be solved by some RL algorithms (DQN, for example, because we have discrete action space). At first, the action space is fixed (the number of actions is $n_1$), ...
5
votes
1answer
81 views

Is the agent aware of a possible different set of actions for each state?

I have a use case where the set of actions is different for different states. Is the agent aware of what actions are valid for each state, or is the agent only aware of the entire action space (in ...
4
votes
1answer
179 views

How should I define the action space for a card game like Magic: The Gathering?

I'm trying to learn about reinforcement learning techniques. I have little background in machine learning from university, but never more than using a CNN on the MNIST database. My first project was ...
4
votes
1answer
2k views

How to deal with different actions for different states of the environment?

I'm new to this AI/Machine Learning and was playing around with OpenAI Gym a bit. When looking through the environments, I came across the Blackjack-v0 environment, ...
3
votes
1answer
665 views

Why does Deep Q Network outputs multiple Q values?

I am learning Deep RL following this tutorial: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8 I understand everything but one detail: This image shows ...
3
votes
1answer
191 views

How does the Alpha Zero's move encoding work?

I am a beginner in AI. I'm trying to train a multi-agent RL algorithm to play chess. One issue that I ran into was representing the action space (legal moves/or honestly just moves in general) ...
3
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1answer
157 views

How to use DQN when the action space can be different at different time steps?

I would like to employ DQN to solve a constrained MDP problem. The problem has constraints on action space. At different time steps till the end, the available actions are different. It has different ...
3
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2answers
369 views

How can I design a reinforcement learning model for a game with multiple complex actions taken at a time?

I have a steady hex-map and turn-based wargame featuring WWII carrier battles. On a given turn, a player may choose to perform a large number of actions. Actions can be of many different types, and ...
2
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1answer
197 views

What should the input and output of the Q-network be in the case of an ordinal action space?

I recently started looking into implementations of the DQN algorithm (e.g. TensorFlow) in some more detail. All the implementations that I found use a network that gives an output for each possible ...
2
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0answers
45 views

When to do discretization to decrease the state/action space in RL?

When to do discretization to decrease the state/action space in RL? Can you give me some references that such a technique is used?
1
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2answers
121 views

What is meant by a multi-dimensional continuous action space?

In the context of Reinforcement Learning, what does it mean to have a multi-dimensional continuous action space? I came across the following in the COBRA Paper A method for learning a distribution ...
1
vote
1answer
294 views

How can I incorporate domain knowledge to choose actions in the case of large action spaces in multi-armed bandits?

Suppose one is using a multi-armed bandit, and one has relatively few "pulls" (i.e. timesteps) relative to the action set. For example, maybe there are 200 timesteps and 100 possible actions....
1
vote
1answer
40 views

How to handle invalid actions for next state in Q-learning loss

I am implementing an RL application in an environment with illegal moves. For handling the illegal moves, I am currently just picking an action as the maximum Q-value from the set of legal Q-values. ...
1
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0answers
18 views

Is there any research on the application of policy gradients to problems where the selection of an action requires the selection of another one?

I am working on a problem and want to explore if it can be solved with PPO (or other policy gradient methods). The problem is that the action space is a bit special, compared to classic RL ...
1
vote
1answer
61 views

Why does each component of the tuple that represents an action have a categorical distribution in the TRPO paper?

I was going through the TRPO paper, and there was a line under Appendix D "Approximating Factored Policies with Neural Networks" in the last paragraph which I am unable to understand The ...
1
vote
1answer
153 views

Should I ignore the actions RIGHTFIRE and LEFTFIRE in the SpaceInvaders environment? [closed]

I'm trying to replicate the DeepMind DQN paper. I'm using OpenAI's Gym. I'm trying to get a decent score with Space Invaders (using SpaceInvaders-v4 environment). I ...
0
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1answer
51 views

How to incorporate action information in the state input of a DQN?

I am working on an RL problem that I am trying to solve using a Deep Q-network. The problem concerns choosing drivers to take specific taxi orders. I am familiar with most of the existing works and ...
0
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0answers
25 views

Task/Process Scheduling Using DQN: Agent Does Not Learn

I have a problem statement where a couple of smartphones, for example inside a shopping mall, can migrate their time-consuming tasks/processes like image processing to an edge server located nearby. ...
0
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0answers
22 views

How to constrain some actions in a multi-dimensional action space?

In portfolio management (allocation) the action space is given by the weights of the assets, i.e. $\sum_{i=1}^m a_i=1$. There may be some weight constraints like one cannot allocate more than 10% of ...
0
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0answers
50 views

Reinforcement learning parameterized action space

I've been working a RL problem with a parameterized action space: there is a finite set of discrete actions $A = \{a_1, a_2, ..., a_k\}$, and each $a_i$ is associated with a set of continuous-valued ...
0
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0answers
39 views

How should I model the state and action spaces for a problem where the goal is to draw a line between two points?

I have a problem where the goal is for the agent to draw a single line between two points on a $500 \times 500$ white image. I have built my DQN. For now, the output layer's size of the network is $[...
0
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0answers
45 views

How to implement RL model with increasing dimensions of state space and action space?

I've read in this discussion that "reinforcement learning is a way of finding the value function of a Markov Decision Process". I want to implement an RL model, whose state space and action ...