Better NN EA - page 12
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What are the best neural network algos for signal filtering?Recurrent? PNN or someting else?
Money...
Neural Network
Neural Network: discussion/development threads
Neural Network: Indicators and systems development
Neural Network: EAs
Neural Network: The Books
The article
CodeBase
The article
Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning
Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models
Previously, we considered hierarchical models for solving problems with, so to speak, the classical approach of the Markov process. However, the advantages of using hierarchical approaches also apply to sequence analysis problems. One such algorithm is the Control Transformer presented in the article "Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling". The method authors position it as a new architecture designed to solve complex control and navigation problems based on reinforcement learning. This method combines modern methods of reinforcement learning, planning and machine learning, which allows us to create adaptive control strategies in a variety of environments.
Control Transformer opens new perspectives for solving complex control problems in robotics, autonomous driving and other fields. I propose to look at the prospects for using this method in solving our trading problems.
Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)
PDT jointly learns an embedding space of future trajectory as well as a future prior conditioned only on past information.. By conditioning action prediction on the target future embedding, PDT is endowed with the ability to "reason over the future". This ability is naturally task-independent and can be generalized to different task specifications.
To achieve efficient online fine-tuning in downstream tasks, you can easily adapt the framework to new conditions by associating each future embedding to its return, which is realized by training a reward prediction network for each future embedding.
Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method
The Decision Transformer and all its modifications, which we discussed in recent articles, belong to the methods of Behavior Cloning (BC). We train models to repeat actions from "expert" trajectories depending on the state of the environment and the target outcomes. Thus, we teach the model to imitate the behavior of an expert in the current state of the environment in order to achieve the target.
Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)
Behavior cloning methods, largely based on the principles of supervised learning, show fairly good results. But their main problem remains the search for ideal role models, which are sometimes very difficult to collect. In turn, reinforcement learning methods are able to work with non-optimal raw data. At the same time, they can find suboptimal policies to achieve the goal. However, when searching for an optimal policy, we often encounter an optimization problem that is more relevant in high-dimensional and stochastic environments.
To bridge the gap between these two approaches, a group of scientists proposed the Distance Weighted Supervised Learning (DWSL) method and presented it in the article "Distance Weighted Supervised Learning for Offline Interaction Data". It is an offline supervised learning algorithm for goal-conditioned policy. Theoretically, DWSL converges to an optimal policy with a minimum return boundary at the level of trajectories from the training set. The practical examples in the article demonstrate the superiority of the proposed method over imitation learning and reinforcement learning algorithms. I suggest taking a closer look at this DWSL algorithm. We will evaluate its strengths and weaknesses in solving our practical problems.
Neural networks made easy (Part 66): Exploration problems in offline learning
As we move along the series of articles devoted to reinforcement learning methods, we are facing the question related to the balance between environmental exploration and exploitation of learned policies. We have previously considered various methods of stimulating the Agent to explore. But quite often, algorithms that demonstrate excellent results in online learning are not so effective offline. The problem is that for offline mode, information about the environment is limited by the size of the training dataset. Most often, the data selected for model training is narrowly targeted as it is collected within a small subspace of the task. This provides an even more limited idea of the environment. However, in order to find the optimal solution, the Agent needs the most complete understanding of the environment and its patterns. We have earlier noted that learning results often depend on the training dataset.
Neural networks made easy (Part 67): Using past experience to solve new tasks