Questions tagged [reinforcement-learning]

For questions related to reinforcement learning, i.e. a machine learning technique where we imagine an agent that interacts with an environment (composed of states) in time steps by taking actions and receiving rewards (or reinforcements), then, based on these interactions, the agent tries to find a policy (i.e. a behavioural strategy) that maximizes the cumulative reward (in the long run), so the goal of the agent is to maximize the reward.

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57 views

Relation between discounted MDP and stochastic shortest path problems in RL

I have been reading about discounted MDPs and Stochastic Shortest Path (SSP). I recently came to know (from a friend) that every discounted MDP can be converted to an equivalent SSP but not the other ...
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1answer
41 views

How to properly resume training of deep Q-learning network?

I'm currently training a deep q-learning network. Due to resource limitations, I am not able to train the model to the desired performance in one go. So what I'm doing now is training the model for a ...
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37 views

Double DQN backpropagation of negative final rewards?

My problem is that in my Double DQN model, negative final rewards are not being backpropagated into action Q-values, and so some Q-values are positive, when they should be negative, and hence ...
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2answers
156 views

With Monte Carlo off-policy learning what do we correct by using importance sampling?

I do not understand the link of importance sampling to Monte Carlo off-policy learning. We estimate a value using sampling on whole episodes, and we take these values to construct the target policy. ...
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1answer
46 views

Why is the logarithm of the standard deviation used in this implementation of proximal policy optimization?

I am currently writing my bachelor thesis, which is an implementation of proximal policy optimization. Sometimes, I hit a wall because of the gaps in my mathematical knowledge. However, implementing ...
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2answers
242 views

How to fight with unstability in self play?

I'm working on a neural network that plays some board games like reversi or tic-tac-toe (zero-sum games, two players). I'm trying to have one network topology for all the games - I specifically don't ...
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1answer
68 views

How to recover the target Q network's weights solely from the snapshots of the primary Q network's weights in DQN?

Suppose that I have a DQN agent, which has two neural networks: one is the primary Q network and the other is the target Q network. In every update, the target Q network is updated with a soft update ...
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8 views

Is it possible to use agent coordinates directly as part of the state

I am working on graph optimisation problem using DQN - the graph is represented as an adjacency matrix and an agent moves through this matrix removing edges between nodes (add a 0) or adding edges ...
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67 views

PyTorch: How to deal with hidden states of an LSTM?

I have a time series in which each date is correlated with the preview one, and base on that I am trying to predict action 1 and action 2. But the problem is that I am not sure how to deal with the <...
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1answer
64 views

Why the optimal Bellman operator of a Q-function can be approximated by a single point

I am currently studying reinforcement learning, especially DQN. In DQN, learning proceeds in such a way as to minimize the norm (least-squares, Huber, etc.) of the optimal Bellman equation and the ...
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35 views

Backpropagation in REINFORCE algorithms with Categorical / Multinomial Distribution

From a paper by Williams, I know in general how to backpropagate log-probabilities of chosen actions when applying the REINFORCE weight update rule. However, I was wondering about a case not being ...
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1answer
60 views

Use Reinforcement Learning instead of genetic algorithm for optimization

I want to use RL instead of genetic or any other evolutionary algorithm in order to find the best parameter for a function. Here is the problem: Given a function $$f(x,y,z,data)$$ x,y and z are some ...
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1answer
272 views

What is ergodicity in a Markov Decision Process (MDP)?

I have read about the concept of ergodicity on the safe RL paper by Moldovan (section 3.2) and the RL book by Sutton (chapter 10.3, 2nd paragraph). The first one says that "a belief over MDPs is ...
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1answer
47 views

How does a model based agent learn the model?

I want to build model-based RL. I am wondering about the process of building the model. If I already have data, from real experience: $S_1, a \rightarrow R,S_2$ $S_2, a \rightarrow R,S_3$ Can I use ...
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1answer
59 views

What is the difference between environment states and agent states in terms of Markov property?

I'm going through the David Silver RL course on YouTube. He talks about environment internal state $S^e_t$, and agent internal state $S^a_t$. We know that state $s$ is Markov if $\mathbb{P}\{S_t=s|S_{...
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52 views

Why do we discount the state distribution?

In Reinforcement Learning, it is common to use a discount factor $\gamma$ to give less importance to future rewards when calculating the returns. I have also seen mention of discounted state ...
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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 ...
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1answer
56 views

What is the advantage of using MCTS with value based methods over value based methods only?

I have been trying to understand why MCTS is very important to the performance of RL agents, and the best description I found was from the paper Bootstrapping from Game Tree Search stating: ...
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39 views

Off-policy Bellman Operators: Writing Operator and Weight Update Function for a 2-State System

I am studying for RL on my own and was trying to solve this question I came across. Write an operator function $T(w, \pi, \mu, l, g)$ that takes weights $w$, a target policy $\pi$, a behaviour policy ...
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1answer
41 views

Given a sequence of states followed by the agent, is it possible to find the Q-value for a state-action pair not in this sequence?

Assume you are given a sequence of states followed by the agent, generated by a random policy, $[s_0, s_1, s_2, \dots, s_n]$. Furthermore, assume the MDP is fully observable and time is discrete. Is ...
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1answer
90 views

Why does the implementation of REINFORCE algorithm minimize the gradient term but not the loss?

I read the book "Foundation of Deep Reinforcement Learning, Laura Graesser and Wah Loon Keng", and when I go through the REINFORCE algorithm, they show the objective function: $$ J\left(\...
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17 views

Understanding advantage estimator in proximal policy optimization

I was reading Proximal Policy Optimization paper. It states following: The advantage estimator used is: $\hat{A}_t=-V(s_t)+r_t+\gamma r_{t+1}+...+\gamma^{T-t+1}r_{T-1}+\color{blue}{\gamma^{T-t}}V(s_T)...
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2answers
105 views

Are there RL algorithms that also try to predict the next state?

So far I've developed simple RL algorithms, like Deep Q-Learning and Double Deep Q-Learning. Also, I read a bit about A3C and policy gradient but superficially. If I remember correctly, all these ...
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13 views

Why has PILCO not been included in Sutton & Barto?

PILCO is a model-based Reinforcement Learning method introduced in 2011 by Deisenroth and Rasmussen. As far as I know, it is still considered one of the most important RL method, especially for its ...
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56 views

How do I implement the cross-entropy-method for a RL environment with a continuous action space?

I found many tutorials and posts on how to solve RL environments with discrete action spaces using the cross entropy method (e.g., in this blog post for the OpenAI Gym frozen lake environment). ...
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1answer
93 views

How to simplify policy gradient theoram to $E_{\pi}[G_t \frac{\nabla_{\theta}\pi(a|S_t,\theta)}{\pi(a|S_t,\theta)}]$?

In "Introduction to Reinforcement Learning" (Richard Sutton) section 13.3(Reinforce algorithm) they have the following equation: \begin{align} \nabla_{\theta}J &\propto \sum_s \mu(s) \...
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13 views

Basic RL code for Production Line Optimization ( Need help )

I am working on how to optimize a Production line given 4 Workstations , 4 Employees(MA) . There are 5 products whose Workpackages need to be completed ( in order ) and these workpackages need to be ...
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29 views

Difference between a distribution model and a sampling environment in Reinforcement Learning

The book from Sutton and Barto define a model in Reinforcement Learning as "something that mimics the behavior of the environment, or more generally, that allows inferences to be made about how ...
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30 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 ...
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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 ...
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1answer
79 views

Reinforcement Learning for an environment that is non-markovian

I am a beginner in the field of Reinforcement Learning with only a couple of months of experience being in the field. Soon, I will start working on a project where we want to optimize the production ...
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16 views

How to have a DNN output a classification for each user at once?

I have a Reinforcement Learning environment with an agent that allocates power values to different users. To do so, I have thought of implementing a deep neural network like the one shown in the ...
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1answer
43 views

Question on identifiability in the “Dueling Network Architectures for Deep Reinforcement Learning” paper

I have two questions on the Dueling DQN paper. First, I have an issue on understanding the identifiability that Dueling DQN paper mentions: Here is my question: If we have given Q-values $Q(s, a; \...
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1answer
29 views

Does a trajectory in reinforcement learning contain the last action?

From what I learn from CS285 and OpenAI's spinning up, a trajectory in RL is a sequence of state-action pairs: $$\tau = \{s_0, a_0, ..., s_t, a_t\}$$ And the resulting trajectory probability is: $$ P(\...
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1answer
20 views

How to have zero value or a value between 200 and 400 in the output of a deep learning model?

I want to implement a DDPG method and obviously, the action space will be continuous. I have three outputs. The first output should be zero or a value between 200 and 400, and the other outputs have ...
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24 views

How is the discounted maximum entropy objective obtained for soft-q-learning and SAC

In the soft q-learning paper, they provide an expression for the maximum entropy objective that takes discounting into account. My main question is: can someone explain how they incorporated ...
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71 views

How to scale the action in a custom environment with DDPG?

I am trying to implement DDPG in a custom gym environment. The action is the relative allocation of funds between each asset. The action space is a Box with the ...
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35 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 ...
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11 views

Do Learning Classifier Systems extend beyond reinforcement learning?

In 2000 the John Holland wrote concerning Learning Classifier Systems (LCS) In recent years there has been a focus on classifier systems as performance systems or evolutionary incarnations of ...
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1answer
69 views

How to avoid being stuck local optima in q-learning and q-network

When using Bellman equation to update q-table or train q-network to fit to greedy max values, the q-values very often get to the local optima and get stuck although randomisation rate ($\epsilon$) ...
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1answer
38 views

Why is the behaviour policy denoted by $\beta$ and the exploration policy by $ \mu'$ in the DDPG paper?

I am learning about the deep deterministic policy gradient (DDPG) (Lillicrap et al, 2016) and got confused about the notation of the behavior policy. Lillicrap et al. denote the policy gradient by $$\...
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0answers
46 views

Train agent to surround a burning fire

I have built a wildfire 'simulation' in unity. And I want to train an RL agent to 'control' this fire. However, I think my task is quite complicated, and I can't work out to get the agent to do what I ...
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7 views

What is the rationale behind the minimap of MAgent?

The MAgent family of PettingZoo is based on a previous implementation that gives a little tutorial explaining the gridworld ...
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2answers
119 views

How is the AI in 3d games implemented?

A few days ago, I started looking a bit more into AI and learning about the way it works, and it is very interesting, but I can't find a clear answer on how the artificial intelligence is implemented ...
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26 views

What is the name of this algorithm that estimates the gradient with an average by sampling from a distribution?

Consider maximizing the function $R(w)$ with parameter $w$ using gradient ascent. However, we don't know the gradient $\nabla_wR(w)$ formula. Now suppose $w$ is sampled from a probability distribution ...
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30 views

Proving existence or non existence of reward function to make given policy “uniquely” optimal when reward function is dependent only on S or both S,A

I was going through paper titled "Algorithms for Inverse Reinforcement Learning" by Andrew Ng and Russell. It states following basics: MDP $M$ is a tuple $(S,A,\{P_{sa}\},\gamma,R)$, where ...
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1answer
76 views

It is mathematically correct to use a Policy Gradient method for 1-step trajectories?

I have come across a Google paper that uses the REINFORCE algorithm (a Policy Gradient Method) for a case where the trajectory of the episodes it proposes would be ...
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1answer
56 views

Q-learning in gridworld with random board

I'm trying to use Q-learning in order to solve Wumpus world environment. Wumpus world is a toy problem on 4x4 gridworld. The agent starts in entry position of the cave, looks for gold (agent can sense ...
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1answer
402 views

How do I get started with multi-agent reinforcement learning?

Is there any tutorial that walks through a multi-agent reinforcement learning implementation (in Python) using libraries such as OpenAI's Gym (for the environment), TF-agents, and stable-baselines-3? ...
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1answer
48 views

How can I compress the states of a reinforcement learning agent?

I'm working on a problem that involves an RL agent with very large states. These states consist of several pieces of information about the agent. The states are not images, so techniques like ...

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