deep rl - What is curriculum learning in reinforcement learning ...
Apr 29, 2023 · Curriculum learning is a general technique for deep learning, which got recently applied to also deep reinforcement learning. It's about designing tasks to guide the learning process of the network or agent. This can be particularly useful to bootstrap the network for very hard problems, and even to achieve better convergence.
Can I speed up NN training by manually guiding training?
Apr 22, 2023 · Such curriculum learning approach has been shown to be beneficial both in deep learning and reinforcement learning problems. Actually there is much interest in automatically building the curriculum : there are some example of this in the literature, allowing to automatically generate a curriculum according to the network's training performance.
How to deal with changing environment in reinforcement learning
Mar 4, 2022 · This is called curriculum learning and the idea is to present easier training examples to the agent at the beginning of training and steadily increase the difficulty of the environment. In turn, the agent will reach the goal in the easier environments, obtain some reward, and learn.
machine learning - What are the limitations of this theoretical ...
Dec 4, 2024 · We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly ...
machine learning - Neural network for game - Artificial …
Nov 6, 2023 · Search for e.g. "reinforcement learning connect 4" to get example grid-based games that will be very similar to solve as yours. You don't have to use neural networks or reinforcement learning. For a game as simple as yours, it is possible to write agents purely on game tree search techniques.
reinforcement learning - Number of states in taxi environment ...
Jul 7, 2019 · These are more sophisticated learning approaches, but it is quite interesting that even very basic RL problems can demonstrate a use for higher level abstractions. Most agents used on the taxi problem don't do this though, as 500 states is really very easy to "brute force" using the simplest algorithms.
machine learning - Neural network with a variable # of neurons ...
Jun 27, 2024 · There are existing learning methods that allow NN architecture to change, but I have not seen any that would change the input layer, since that is usually defined by the problem being solved - so it appears you want to change the data or problem, perhaps as a form of curriculum learning (where you start with a simple problem and use that to ...
deep learning - How Come My (D)DQN Fails To Learn? - Artificial ...
Jan 19, 2022 · You can apply curriculum learning to make the environment less ambiguous during early training. The principle of this strategy is to provide easier training examples which do not require many random actions to reach the goal. As the agents learn the complexity of the environment is increased and it can transfer knowledge.
What is teacher forcing? - Artificial Intelligence Stack Exchange
Feb 13, 2020 · Recall that supervised learning can also be thought of as learning with a teacher. Hence the expression "teacher forcing", i.e. you force the predictions to be based on correct histories (the teacher's labels).
comparison - What are the pros and cons of sparse and dense …
Aug 13, 2020 · Imitation learning is one of many techniques available to work around or deal with problems that have sparse reward structure. Others include: Reward shaping, which attempts to convert a sparse reward scheme to a dense one using domain knowledge of the researcher.