![]() A2C / A3C, which performs gradient ascent to directly maximize performance,.Policy optimization also usually involves learning an approximator for the on-policy value function, which gets used in figuring out how to update the policy.Ī couple of examples of policy optimization methods are: This optimization is almost always performed on-policy, which means that each update only uses data collected while acting according to the most recent version of the policy. They optimize the parameters either directly by gradient ascent on the performance objective, or indirectly, by maximizing local approximations of. Methods in this family represent a policy explicitly as. There are two main approaches to representing and training agents with model-free RL: As of the time of writing this introduction (September 2018), model-free methods are more popular and have been more extensively developed and tested than model-based methods. While model-free methods forego the potential gains in sample efficiency from using a model, they tend to be easier to implement and tune. Model-learning is fundamentally hard, so even intense effort-being willing to throw lots of time and compute at it-can fail to pay off.Īlgorithms which use a model are called model-based methods, and those that don’t are called model-free. The biggest challenge is that bias in the model can be exploited by the agent, resulting in an agent which performs well with respect to the learned model, but behaves sub-optimally (or super terribly) in the real environment. If an agent wants to use a model in this case, it has to learn the model purely from experience, which creates several challenges. The main downside is that a ground-truth model of the environment is usually not available to the agent. When this works, it can result in a substantial improvement in sample efficiency over methods that don’t have a model. A particularly famous example of this approach is AlphaZero. Agents can then distill the results from planning ahead into a learned policy. The main upside to having a model is that it allows the agent to plan by thinking ahead, seeing what would happen for a range of possible choices, and explicitly deciding between its options. By a model of the environment, we mean a function which predicts state transitions and rewards. The basic idea behind diffusion models is rather simple.One of the most important branching points in an RL algorithm is the question of whether the agent has access to (or learns) a model of the environment. This iterative process makes them slow at sampling, at least compared to GANs. To some extent, this idea of refining the representation has already been used in models like alphafold. ![]() The intuition behind this is that the model can correct itself over these small steps and gradually produce a good sample. Intuitively, they aim to decompose the image generation process (sampling) in many small “denoising” steps. Various other approaches will be discussed to a smaller extent such as stable diffusion and score-based models.ĭiffusion models are fundamentally different from all the previous generative methods. We will focus on the most prominent one, which is the Denoising Diffusion Probabilistic Models (DDPM) as initialized by Sohl-Dickstein et al and then proposed by Ho. There are already a bunch of different diffusion-based architectures. In this blog post, we will dig our way up from the basic principles. Example architectures that are based on diffusion models are GLIDE, DALLE-2, Imagen, and the full open-source stable diffusion.īut what is the main principle behind them? ![]() They have already attracted a lot of attention after OpenAI, Nvidia and Google managed to train large-scale models. Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.
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