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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180 views

What is the proof that "reward-to-go" reduces variance of policy gradient?

I am following the OpenAI's spinning up tutorial Part 3: Intro to Policy Optimization. It is mentioned there that the reward-to-go reduces the variance of the policy gradient. While I understand the ...
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123 views

If the minimum Q value is decreasing and the maximum Q value increasing, is this a sign that dueling double DQN is diverging?

I'm training a dueling double DQN agent with prioritized replay buffer and notice that the min Q values are decreasing, while the max Q values are increasing. Is this a sign that it is diverging? ...
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1answer
175 views

How to implement RAM versions of Atari games

I have coded the breakout RAM version, but, unfortunately, its highest reward was 5. I trained it for about 2 hours and never reached a higher score. The code is huge, so I can't paste here, but, in ...
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79 views

How can the target rely on untrained parameters?

I'm trying to understand DQN. I understand where the loss function comes from. I'm just unsure about why the target function works in practice. Given the loss function $$ L_i(\theta_i) = [(y_i - Q(s,a;...
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59 views

Are the final states not being updated in this $n$-step Q-Learning algorithm?

I am reading this paper and in algorithm 3 they describe an $n$-step Q-Learning algorithm. Below is the pseudo-code. From this pseudo-code, it looks as though the final tuples that they would ...
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102 views

How to prevent deep Q-learning algorithms to overfit?

I have recently solved the Cartpole problem using double deep Q-learning. When I saw how the agent was doing, it used to go right every time, never left, and it did similar actions all the time. Did ...
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47 views

Can you find another reason for sample inefficiency of model-free on-policy Deep Reinforcement Learning?

The following mindmap gives an overview of multiple reasons for sample inefficiency. The list is definitely not complete. Can you see another reason not mentioned so far? Some related links: ...
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53 views

Can weighted importance sampling be applied to off-policy evaluation for continuous state space MDPs?

Can weighted importance sampling (WIS) and importance sampling (IS) be applied to off-policy evaluation for continuous state spaces MDPs? Given that I have trajectories of $(s_t,a_t)$ pairs and the ...
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93 views

Does the concept of validation loss apply to training deep Q networks?

In deep learning, the concept of validation loss is to ensure that the model being trained is not currently overfitting the data. Is there a similar concept of overfitting in deep q learning? Given ...
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34 views

Is there an online RL algorithm that receives as input a camera frame and produces an action as output?

I want to build a reinforcement learning model, which takes a camera picture as input, that learns online (in terms of machine learning). Based on the position of an object on the camera, I want the ...
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72 views

Can we use a Gaussian process to approximate the belief distribution at every instant in a POMDP?

Suppose $x_{t+1} \sim \mathbb{P}(\cdot | x_t, a_t)$ denotes the state transition dynamics in a reinforcement learning (RL) problem. Let $y_{t+1} = \mathbb{P}(\cdot | x_{t+1})$ denote the noisy ...
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1answer
96 views

Do smaller loss values during DQN training produce better policies?

During the training of DQN, I noticed that the model with prioritized experience replay (PER) had a smaller loss in general compared to a DQN without PER. The mean squared loss was an order of ...
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46 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?
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492 views

Why is it hard to prove the convergence of the deep Q-learning algorithm?

Why is it hard to prove the convergence of the DQN algorithm? We know that the tabular Q-learning algorithm converges to the optimal Q-values, and with a linear approximator convergence is proved. ...
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1answer
164 views

How long should the state-dependent baseline for policy gradient methods be trained at each iteration?

How long should the state-dependent baseline be trained at each iteration? Or what baseline loss should we target at each iteration for use with policy gradient methods? I'm using this equation to ...
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51 views

Can a typical supervised learning problem be solved with reinforcement learning methods?

Let's say I want to teach a neural to classify images, and, for some reason, I insist on using reinforcement learning rather than supervised learning. I have a dataset of images and their matching ...
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86 views

PPO algorithm converges on only one action

I have taken some reference implementations of PPO algorithm and am trying to create an agent which can play space invaders . Unfortunately from the 2nd trial onwards (after training the actor and ...
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137 views

What is the original source of the TD Advantage Actor-Critic algorithm?

What is the original source of the TD Advantage Actor-Critic algorithm? I found this tutorial really helpful for learning the algorithm. However, what is the original source of this algorithm?
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1answer
71 views

Adversarial Q Learning should use the same Q Table?

I'm creating a RF Q-Learning agent for a two player fully-observable board game and wondered, if I was to train the Q Table using adversarial training, should I let both 'players' use, and update, the ...
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108 views

What is the gradient of the Q function with respect to the policy's parameters?

I have been recently studying Actor-Critic algorithms, and I ran into the following question. Let $Q_{\omega}$ be the critic network, and $\pi_{\theta}$ be the actor. It is known that in order to ...
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64 views

How are the classical MDP and the object-oriented MDP views different?

I've been reading the attached paper - which aims to model entities in the world as objects, including the learning agent itself! To say the least, the goal is to navigate through what seems like a ...
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39 views

How should I design a reward function for a NLP problem where two models interoperate?

I would like to design a reward function. I am training two models from the first model that classify set of texts (paragraphs and keywords) and I also got some hidden states. The second model is ...
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89 views

Action masking for on policy algorithm like PPO

I have an environment, in which my agent learns according to PPO. The environment has a maximum of 80 actions, however not all of them are always allowed. My idea was to mask them, by setting the ...
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33 views

How can I formalise a non-zero-sum game of $N$ agent as Markov game?

I coded a non-zero-sum game of $N$ agents in a discrete dynamic environment to RL with Q-learning and DQN agents. It's like a marathon. Only two actions are available per agent: $\{ G \text{ (move ...
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50 views

Is Mean Squared Error Loss function a good loss function for continuous variables $0 < x < 1$

Suppose I am utilising a neural network to predict the next state, $s'$ based on the current $(s, a)$ pairs. all my neural network inputs are between 0 and 1 and the loss function for this network ...
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96 views

NaNs after a while in training of PPO

My problem is that every time I am trying to train my PPO agent I get NaN values after a while. The diagnostic that I get is the following: ...
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35 views

How was the DQN trained to play many games?

Some people claim that DQN was used to play many Atari games. But what actually happened? Was DQN trained only once (with some data from all games) or was it trained separately for each game? What was ...
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29 views

How should I define the reward function for the Connect Four game?

I'm creating an RL application for the game Connect Four. I've researched the different strategies for the game and which positions are more favourable to lead to a win. Should I be assigning ...
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137 views

Whats the correct loss function to use during deep Q-learning (discrete action space)

After playing around with normal Q-learning I have decided to switch to deep Q-learning and I have encountered this problem. As I understand, for a task with discrete action space, where there are 4 ...
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60 views

Why is my DQN model not getting better?

I've created a deep Q network. My model does not get better, and can't see what I'm doing wrong. I'm new to RL. Replay Memory ...
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121 views

Policy Gradient Reward Oscillation in MATLAB

I'm trying to train a Policy Gradient Agent with Baseline for my RL research. I'm using the in-built RL toolbox from MATLAB (https://www.mathworks.com/help/reinforcement-learning/ug/pg-agents.html) ...
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40 views

How to draw backup diagram when reward is in expectation but next state is iterated?

I am working through Sutton and Barto's RL book. So far in the text, when backup diagrams are drawn, the reward and next state are iterated together (i.e. the equations always have $\sum_{s',r}$), ...
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49 views

Should the RL agent be trained in an environment with real-world data or with a synthetic model?

I want to train a reinforcement learning agent in an environment with parameters (for example, the wind speed, sun irradiation, etc.) that change over time. I have recorded a limited amount of data ...
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1answer
117 views

How can I implement the reward function for an 8-DOF robot arm with TRPO?

I need to get an 8-DOF (degrees of freedom) robot arm to move a specified point. I need to implement the TRPO RL code using OpenAI gym. I already have the gazebo environment. But I am unsure of how to ...
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241 views

How does adding noise to the action in DDPG help in learning?

I can't understand how playing with the action generated by the actor network in DDPG by adding the noise term helps in exploration.
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1answer
281 views

Why isn't my implementation of DQN using TensorFlow on the FrozenWorld environment working?

I am trying to test DQN on FrozenWorld environment in gym using TensorFlow 2.x. The update rule is (off policy) $$Q(s,a) \leftarrow Q(s,a)+\alpha (r+\gamma~ max_{a'}Q(s',a')-Q(s,a))$$ I am using an ...
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83 views

Formulation of a Markov Decision Process Problem

Given a list of $N$ questions. If question $i$ is answered correctly (given probability $p_i$), we receive reward $R_i$; if not the quiz terminates. Find the optimal order of questions to maximize ...
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20 views

What effect does increasing the actions in RL have?

Consider a 2D snake game, where the snake has to eat food to become longer. It must avoid hitting walls and biting into her tail. Such a game could have a different amount of actions: 3 actions: go ...
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210 views

How does the update rule for the one-step actor-critic method work?

Can you please elucidate the math behind the update rule for the critic? I've seen in other places that just a squared distance of $R + \hat{v}(S', w) - \hat{v}(S,w)$ is used, but Sutton suggests an ...
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50 views

Can the importance sampling estimator have a non-stationary behaviour policy even if the target policy is stationary?

The inverse propensity score (IPS) estimator, which is used for off-policy evaluation in a contextual bandit problem, is well explained in the paper Doubly Robust Policy Evaluation and Optimization. ...
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42 views

Why is this Monte Carlo approach scalable for a growing number of states variables and action variables?

I am reading a research paper on the formulation of MDP problems to ICU treatment decision making: Treatment Recommendation in Critical Care: A Scalable and Interpretable Approach in Partially ...
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1answer
112 views

NoisyNet DQN with default parameters not exploring

I implemented a DQN algorithm that plays OpenAIs Cartpole environment. The NN architecture consists of 3 normal linear layers that encode the state, and one noisy linear layer, that predicts the Q ...
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45 views

Can we use imitation learning for on-policy algorithms?

Imitation learning uses experiences of an (expert) agent to train another agent, in my understanding. If I want to use an on-policy algorithm, for example, Proximal Policy Optimization, because of it'...
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40 views

What are some approaches to estimate the transition and observation probabilities in POMDP?

What are some common approaches to estimate the transition or observation probabilities, when the probabilities are not exactly known? When realizing a POMDP model, the state model needs additional ...
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48 views

Should I consider mean or sampled value for action selection in ppo algorithm?

When considering the policy network in PPO algorithm, we need to fit a Gaussian distribution to the neural network output (for a continuous action space problem). When I use this network to obtain ...
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0answers
48 views

What form of output would be needed to train a model on a connect 4 AI?

I've had a big interest in machine learning for a while, and I've followed along a few tutorials, but have never made my own project. After losing many games of connect 4 with my friends, I decided to ...
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0answers
81 views

How would one develop an action space for a game that is proprietary?

I'm currently trying to develop an RL that will teach itself to play the popular fighting game "Tekken 7". I initially had the idea of teaching it to play generally- against actual opponents with ...
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0answers
51 views

How to set the multiple continuous actions with constraints

I want to build a Deep Reinforcement Learning Model for Asset allocation. Background: I have 7 stock indexes from different markets, and I want to build a policy to produce the action (likes whether ...
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41 views

What could be the cause of the drop of the total reward when using DQN to solve the cart-pole environment?

I'm trying to use DQN to solve the cart-pole environment. I have 2 networks (target and behavior). Both of them have 3 hidden layers with 24 neurons, using the ReLU activation. The loss is MSE and the ...
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0answers
27 views

Finding total number of states in a POMDP

I've been working on a question that is posed in a document I've been reading, that models qualifying for a job as a POMDP. In this model, a person takes 3 exams, and must pass all of them in order to ...

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