Questions tagged [reinforcement-learning]

Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

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22 views

Trouble understanding the partial differentiation used in reinforcement learning

I am studying deterministic actor-critic algorithms in reinforcement learning. I try to give a brief explanation of actor-critic algorithms before jumping into the mathematics. The actor takes in ...
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+50

Actor-critic architecture: How is the policy updated?

I am going through the ddpg baseline code to try and gain an intuitive understanding of how the actor and critic networks function. DDPG has two components: the actor which is the deterministic ...
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15 views

Assumptions on discounted long-term loss

The infinite horizon discounted long-term loss is defined as: $$ f(\theta) = \mathbb{E}_{\tau \sim \mathbb{P}(.|\theta)}\left[\sum_{t=1}^{\infty}{\gamma^t l_m(s_t,a_t)}\right]$$ where $(s_t,a_t) \in ...
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8 views

exploitation vs. exploration: upper-confidence-bound vs. epsilon-greedy

I am looking into some different ways for doing exploitation vs. exploration (e.g. multi-arm bandit problem). There are approaches like upper_confidence_bound and epsilon-greedy. I am wondering what ...
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24 views

reinforcement learning: PPO vs. DDPG vs. TRPO - difference and intuition

I know there is a lot of blog talk about the PPO, DDPG and TRPO, but I am wondering would it be possible to explain the differences of these methods in layman's term? What's the intuition behind them ...
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9 views

Rewards are converged but with a lot of variations

I am training a reinforcement learning agent on an episodic task of fixed episode length. I am tracking the training process by plotting the cumulative rewards over an episode. I am using tensorboard ...
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0answers
10 views

Deep Q Learning - training slows down significantly

I'm trying to build a deep Q network to play snake. I designed the game so that the window is 600 by 600 and the snake's head moves 30 pixels each tick. I implemented the DQN algorithm with memory ...
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9 views

Autonomous learning - chatbots

My chatbots need to be trained when we get new data or feedbacks from users. Can someone provides ways how these chatbots can learn on themselves and become intelligent day by day? Some of techniques ...
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1answer
20 views

Reinforcement Learning in NLP for chatbots

Is anyone aware of any successful implementation of reinforcement learning for NLP. I am looking to for chatbots which can learn automatically. Tried searching internet but found very few articles ...
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12 views

using reinforcement learning for classification

this is a purely conceptual query and in case the moderators feel it needs to be asked elsewhere, i would be happy to move it there. We do a lot of work in text classification and a senior ...
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1answer
23 views

Having a reward structure which gives high positive rewards compared to the negative rewards

I am training an RL agent using PPO algorithm for a control problem. The objective of the agent is to maintain temperature in a room. It is an episodic task with episode length of 9 hrs and step size(...
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9 views

DNN loss gets smaller but accuracy stays the same

I am learning a DeepNN to choose between three decisions in a simulation. Therefore, I can run the simulation as often as I want and can generate as many samples as I want. Based on this tutorial (...
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1answer
14 views

Formulation of a reward structure

I am new to reinforcement learning and experimenting with training of RL agents. I have a doubt about reward formulation, from a given state if a agent takes a good action i give a positive reward, ...
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8 views

Several dips in accumulated episodic rewardss during training of a reinforcement learning agent

Hi I am training reinforcement learning agents for a control problem using PPO algorithm. I am tracking the accumulated rewards for each episode during the training process. Several times during the ...
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13 views

Actor Critic Model implementation

I am going to work on a project which requires implementation of A2C model using Tensorflow 2.0. I am new in the Machine Learning field and also in Python. These are topics which I have covered ...
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6 views

two PPO implentations

I have uploaded here my PPO implentation from scratch: https://github.com/MakisKans/Reinforcement_Learning/tree/master/PPO In the PPO.py you can find two functions : learn and train. The former is ...
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15 views

What reinforcement learning algorithm to choose for self-driving car

I have a car that has three sensors at the front. Using these sensors only I want to let it learn to drive on a track. I'm new to reinforcement learning, but I was thinking about using the Q-learning ...
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1answer
17 views

Training a model that has both 2D and 1D features using a CNN

I'm looking to pre-train a model for an RL agent but I'm having some trouble figuring some stuff out. Dataset: Minerl MineRLNavigateDense-v0 The observation space includes : 2D screen input (64,64) ...
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24 views

Applying Reinforcement Learning in the following scenario

I'm working on a scenario/environment where I have a simulation that provides an arrangement or results of the simulation that has data in a format of samples in vectors(x,y,z,N). Let's say it maps ...
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1answer
18 views

How to handle differences between training and deploying of an RL agent

Hi I am training an RL agent for a control problem. The objective of the agent is to maintain temperature in a zone. It is an episodic task with episode length of 10 hrs and actions being taken every ...
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0answers
19 views

How can I increase the speed and performance of my implementation of an AI for Reversi?

I made an AI for Reversi, aka Othello (8×8), like Alpha Zero, using this book. This book is written in Japanese. The source code of the AI I implemented can be found in this Github repository. There ...
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14 views

Prerequisites for reinforcement learning for DTR

I want to learn how RL is used for Dynamic Treatment Regimes. What are some must-read articles or papers? Also, I am relatively new to RL and am currently reading Sutton's book on the topic. Lets say ...
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10 views

saving a model during training of an RL agent

I am training an RL agent using PPO2 algorithm. Iam using stable-baselines library. During the training process, my rewards are slowly increasing and stabilizing, but are falling down suddenly. I ...
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19 views

DQN vs PG - when to use which?

I'd like to understand when using PG methods is more adequate than using DQN methods. Just to give a bit of background: I am currently using both APEX and R2D2 for my projects. Both work very well in ...
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32 views

Sudden decline in cumulative reward of an reinforcement learning training agent

Hi I am training an RL agent using PPO algorithm of stable baselines library. I have integrated my training with tensorboard to monitor the training process. Over a period of time my training rewards ...
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1answer
24 views

Different results every time I train a reinforcement learning agent

I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it. The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
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0answers
10 views

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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9 views

How to use deep reinforcement learning to learn how to play Checkers?

I am a student new in reinforcement learning and I'm trying to implement an AI able to play Checkers. I want to implement a deep learning solution. However, I am confused on how to do that. I ...
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15 views

What's the input for the cost function?

I'm trying to implement deep Q-learning, but I do not know what to put into the cost function. My net has 8 scalar inputs, 4 scalar outputs (from 0-1) and no hidden layers. To calculate the cost I ...
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Best DDPG use case when having sensor historical data and virtual environment of a real world system

I have a historical data (from real sensors) which have enough knowledge of the actions and states needed for the use of reinforcement learning and a modeled virtual environment of a real system (...
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6 views

Learning using DDPG with pyhton solely using historical data

I have a lengthy timeseries datasets which contains several variables (from sensors etc) to be classified as actions or states. Providing they are successfully done, I want to learn a control policy ...
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1answer
35 views

Evaluating a trained Reinforcement Learning Agent?

I am new to reinforcement learning agent training. I have read about PPO algorithm and used stable baselines library to train an agent using PPO. So my question here is how do I evaluate a trained RL ...
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Transitioning from Math PhD to ML research [closed]

I am currently a Math PhD about to defend in January. I work in a field in functional analysis that uses a lot of measure theory (but no stats). I have been considering transitioning careers since I ...
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2answers
20 views

Deep Q network disregards input, giving identical output no matter the input state

I've creating a very simple game, The board is an array of size 6. 0 is "empty cell" , 5 is "Goal", 8 is "player location" [8 0 0 5 0 0] for example means the agent needs to move 2 "right" to win. ...
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0answers
12 views

What are regret bounds?

I searched for the term and it appears in a few articles but it is used without explanation. The only explanation I could find is in a PhD thesis: "Regret bounds are the common thread in the analysis ...
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12 views

RL library for implementing PPO algorithm

I have recently studied about PPO algorithm. I want to train an RL agent using PPO. However I am finding it difficult to implement. So I am planning to use a library which will help me in the ...
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1answer
29 views

transform a supervised neural network to reinforcement learning?

I have a functional LSTM model that works with an acceptable performance. How can I now convert this supervised model to a reinforcement learning model for improving the performane? Is there any ...
0
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1answer
32 views

Policy Gradient with continuous action space

How to apply reinforce/policy-gradient algorithms for continuous action space. I have learnt that one of the advantages of policy gradients is , it is applicable for continuous action space. One way I ...
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0answers
10 views

Major drop in DeepQNetwork agent after certain number of iteration

I've built a game with an agent moving around trying to collect gold (+10 reward) and it dies when it hits a wall (-100, terminal condition) and -1 for any other step that is not gold nor wall. My ...
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1answer
37 views

Deep reinforcement learning on changing data sizes

I have a game that I want to build a model that will learn to play the game. Yet, the environment output is two lists that represent the location and number of soldiers of the user and Opponent. The ...
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2answers
103 views

Agent always takes a same action in DQN - Reinforcement Learning

I have trained an RL agent using DQN algorithm. After 20000 episodes my rewards are converged. Now when I test this agent, the agent is always taking the same action , irrespective of state. I find ...
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0answers
18 views

Policy Gradient custom loss function not working

I was experimenting with my policy gradient reinforcement learning algorithm, and I was wondering if I could use a similar method to the supervised cross-entropy. So, instead of using existing labels, ...
0
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1answer
24 views

Reward is converging but actions taken by trained agent are illogical in reinforcement learning

I am training a reinforcement learning agent using DQN. My state space has 6 variables and the agent can one action which is discretized into 500 actions My reward structure looks like ...
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0answers
18 views

How to teach algorithm to mimic paths in a certain enviroment

I have a set of scenarios which represent the movement of a car in a certain environment containing some obstacles. So for each scenario I have the position of the car (x,y,t) and a description of the ...
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1answer
77 views

How to avoid overfitting in Reinforcement Learning

I have implemented a RL model based on Deep Q-Learning for learning how to play a 2D game, like the ones in the OpenAI Gym. For testing the model, unlike most people, I have chosen to evaluate its ...
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0answers
13 views

How to calculate Temperature variable in softmax(boltzmann) exploration

Hi I am developing a reinforcement learning agent for a continous state/discrete action space. I am trying to use boltmzann/softmax exploration as action selection strategy. My action space is of size ...
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0answers
13 views

Python Framework for Evolutionary Strategies Deep Reinforcement Learning

I am looking to experiment with deep reinforcement learning. In particular training with evolutionary strategies. Which python framework is most suited to this? From what I can gather so far: ...
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12 views

temperature variable in boltzmmann-exploration in reinforcement learning

I have been using epsilon greedy action selection strategy and recently have come across boltzmann(softmax) action selection strategy. One thing I am not clear about boltzmann exploration is the ...
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68 views

boltzmann-exploration(softmax exploration) in reinforcement learning

I have started learning reinforcement learning and as a part of it I am exploring the action selection strategies available. I am comparing epsilon-greedy vs boltzmann exploration(softmax exploration)....