Questions tagged [reinforcement-learning]

For questions related to learning controlled by external positive reinforcement or negative feedback signal or both, where learning and use of what has been thus far learned occur concurrently.

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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. $n$-step q-learning"> From this pseudo-code, it looks as though the final tuples ...
22 views

What is meant by the rank of the scoring function here?

I've been reading this paper on Knowledge Graph Reasoning for Explainable Recommendation lately, and I don't understand a particular section: Specifically, the scoring function $f((r,e)|u)$ maps ...
27 views

Is there any programming practice website for beginners in Reinforcement Learning [closed]

I am doing an online course on Reinforcement Learning from university of Alberta. It focus too much on theory. I am engineering and I am interested towards applying RL to my applications directly. ...
23 views

Calculating the advantage 'gain' of actions in model-free reinforcement learning

I have a simple question about model-free reinforcement. In a model I'm writing about, I want to know the value 'gain' we'd get for executing an action, relative to the current state. That is, what ...
4 views

Learning to select a subgraph via reinforcement learning?

I have the following problem: I am given a graph with a lot (>30000) nodes. Nodes are associated with a low (<10)-dimensional feature vector, and edges are associated with a low (<10)-...
24 views

Can the agent wait until the end of the episode to determine the reward in SARSA?

From Sutton and Barto's book Reinforcement Learning (Adaptive Computation and Machine Learning series) (p. 99), the following definition for first-visit MC prediction, for estimating $V \sim V_\pi$ is ...
67 views

What is a RAM state in the gym's breakout-ram environment?

I have encountered the gym environment and decided to create AI that plays breakout. Here is the link: https://gym.openai.com/envs/Breakout-ram-v0/. The documentation says that the state is ...
51 views

Can Q-learning converge even if it doesn't explore all state-action pairs?

My understanding of Q-learning is that it essentially builds a dictionary of state-action pairs, so as to maximize the Markovian (i.e., step-wise, history-agnostic?) reward. This incremental update of ...
28 views

Actor-Critic implementation not learning

I've implemented a vanilla actor-critic and have run into a wall. My model does not seem to be learning the optimal policy. The red graph below shows its performance in cartpole, where the algorithm ...
28 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 ...
33 views

If deep Q-learning starts to choose only one action, is this a sign that the algorithm diverged?

I'm working on a deep q-learning model in an infinite horizon problem, with a continous state space and 3 possible actions. I'm using a neural network to approximate the action-value function. ...
52 views

Is the distribution of state-action pairs from sample based planning accurate for small experience sets?

From the David Silver's lecture 8: Integrating Learning and Planning - based on Sutton and Barto - he talks about using sample-based planning to use our model to take a sample of a state and then use ...
30 views

Why is learning $s'$ from $s,a$ a kernel density estimation problem but learning $r$ from $s,a$ is just regression?

In David Silver's 8th lecture he talks about model learning and says that learning $r$ from $s,a$ is a regression problem whereas learning $s'$ from $s,a$ is a kernel density estimation. His ...
295 views

Is there any good reference for double deep Q-learning?

I am new in reinforcement learning, but I already know deep Q-learning and Q-learning. Now, I want to learn about double deep Q-learning. Do you know any good references for double deep Q-learning? ...
22 views

What are finite horizon look-ahead policies in reinforcement learning?

I was reading the paper How to Combine Tree-Search Methods in Reinforcement Learning published in AAAI Conference 2019. It starts with the sentence Finite-horizon lookahead policies are abundantly ...
37 views

How should I decay $\epsilon$ in Q-learning?

How should I decay the $\epsilon$ in Q-learning? Currently, I am decaying epsilon as follows. I initialize $\epsilon$ to be 1, then, after every episode, I multiply it by some $C$ (let it be $0.999$)...
35 views

Understanding the role of the target network in this DQN algorithm

I've found online this interesting algorithm: From what I understand reading this algorithm, I can't figure out why I should "perform the opposite action" and consequently storing that second ...
112 views

Is this proof of $\epsilon$-greedy policy improvement correct?

The text book being referred to, in this question is "Reinforcement Learning: An introduction" by Richard Sutton and Andrew Barto (second edition, 2018). For your convenience, I have enclosed the ...
114 views

Is there any good source for when the pole actually starts all the way at the bottom, in the cartpole problem?

There are a lot of examples of balancing a pole (see image below) using reinforcement learning, but I find that almost all examples start close to the upright position. Is there any good source (or ...
36 views

Using a model-based method to build an accurate day trading environment model

There are several different angles we can classify Reinforcement Learning methods from. We can distinguish three main aspects : Value-based and policy-based On-policy and off-policy Model-free and ...
18 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: ...
19 views

Why do we also need to normalize the action's values on continuous action spaces?

I was reading here tips & tricks for training in DRL and I noticed the following: always normalize your observation space when you can, i.e., when you know the boundaries normalize your ...
35 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 ...
68 views

How do I convert an MDP with the reward function in the form $R(s,a,s')$ to and an MDP with a reward function in the form $R(s,a)$?

The AIMA book has an exercise about showing that an MDP with rewards of the form $r(s, a, s')$ can be converted to an MDP with rewards $r(s, a)$, and to an MDP with rewards $r(s)$ with equivalent ...
93 views

Handle non-existing states in q-learning

I am using Q-learning to solve an engineering problem. The objective is to generate a Q-table associating state to Q-values. I created a State vector ...
39 views

How to create MDP (RL) environment for custom problem?

I am trying to solve the scheduling of resources problems using RL/GA. I am stuck on how to create a custom environment for the problem and actually carry out some tests. I read and implemented Q-...
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How would researchers determine the best deep learning model if every run of the code yields different results?

There are many factors that cause the results of ML models to be different for every run of the same piece of code. One factor could be different initialization of weights in the neural network. ...
174 views

Why is the policy not a part of the MDP definition?

I'm reading an article on reinforcement learning, and I don't understand why the agent's policy $\pi$ is not part of definition of Markov Decision process(MDP): Bu, Lucian, Robert Babu, and ...
34 views

What is the relation between multi-agent learning and reinforcement learning?

What is the relation between multi-agent learning and reinforcement learning? Is one a sub-field of the other? For instance, would it make sense to state that your research interest are multi-agent ...
28 views

How can I perform policy update in python? [closed]

I'm using Python and tensorflow to implement a Deep Q-learning with experience replay in a continous action and state spaces and I have used two neural networks to approximate both the policy function ...
83 views

I am working on scheduling problem that has inherent randomness. The dimensions of action and state spaces are 1 and 5 respectively. I am using DDPG, but it seems extremely unstable, and so far it ...
42 views

Convergence of a delayed policy update Q-learning

I thought about an algorithm that twists the standard Q-learning slightly, but I am not sure whether convergence to the optimal Q-value could be guaranteed. The algorithm starts with an initial ...
31 views

Policy Gradient on Tic-Tac-Toe not working

I wanted to implement the Policy Gradient on Tic-Tac-Toe. I tried to use the code that worked for any environment like CartPole-v0 to my Tic-Tac-To game. But it is not learning. There are no errors. ...
26 views

In vanilla policy gradient is the baseline lagging behind the policy?

Vanilla policy gradient algorithm (using baseline to reduce variance) acc to here (page 16) Initialize policy parameter θ, baseline b for iteration=1, 2, . . . do Collect a set of ...
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Pytorch XLA to solve the spawn problems in a Colab Env [migrated]

It seems that torch.multiprocessing.set_start_method("spawn") can't be used in an Colab Env. Only 'fork' is allowed. I have implemented A3C - data parallelism to ...
38 views

Can we increase the speed of training a reinforcement learning algorithm?

I am new in reinforcement learning. I started reading the PyTorch's documentation about the cart pole control. Whenever an agent fails, they restart the environment. When I run the code, the time in ...
57 views

What is the intuition behind importance sampling for off-policy value evaluation?

The technique for off-policy value evaluation comes from importance sampling, which states that $$E_{x \sim q}[f(x)] \approx \frac{1}{n}\sum_{i=1}^n f(x_i)\frac{q(x_i)}{p(x_i)},$$ where $x_i$ is ...
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Do we have two Q-learning update formulas?

I have seen two deep Q-learning formulas: Q\left(S_{t}, A_{t}\right) \leftarrow Q\left(S_{t}, A_{t}\right)+\alpha\left[R_{t+1}+\gamma \max _{a} Q\left(S_{t+1}, a\right)-Q\left(S_{t}, A_{t}\right)\...
34 views

Learning policy where action involves discrete and continuous parameters

Typically it seems like reinforcement learning involves learning over either a discrete or a continuous action space. An example might be choosing from a set of pre-defined game actions in Gym Retro ...
62 views

What are the conditions of convergence of temporal-difference learning?

In reinforcement learning, temporal difference seem to update the value function in each new iteration of experience absorbed from the environment. What would be the conditions for temporal-...
19 views

How can I increase the exploration in the Proximal Policy Optimation algorithm?

How can I increase the exploration in the Proximal Policy Optimation reinforcement learning algorithm? Is there a variable assigned for this purpose? I'm using the stable-baseline implementation: ...
60 views