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OpenAI published a paper called Evolution Strategies as a Scalable Alternative to Reinforcement Learning where they showed that evolution strategies, while being less data efficient than RL, offer many benefits. For these problems, rather than rely on a very noisy and possibly meaningless gradient estimate of the future to our policy, we might as well just ignore any gradient information, and attempt to use black-box optimisation techniques such as genetic algorithms (GA) or ES.
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Sometimes we are given just a single reward, like a bonus check at the end of the year, and depending on our employer, it may be difficult to figure out exactly why it is so low. In the real world, rewards can be sparse and noisy. However, credit assignment is still difficult when the reward signals are sparse. Even if we are able to calculate accurate gradients, there is also the issue of being stuck in a local optimum, which exists many for RL tasks.Ī whole area within RL is devoted to studying this credit-assignment problem, and great progress has been made in recent years. However, it is not trivial to estimate the gradient of reward signals given to the agent in the future to an action performed by the agent right now, especially if the reward is realised many timesteps in the future. For example, in reinforcement learning (RL) problems, we can also train a neural network to make decisions to perform a sequence of actions to accomplish some task in an environment. However, there are many problems where the backpropagation algorithm cannot be used. With these gradients, we can efficiently search over the parameter space to find a solution that is often good enough for our neural net to accomplish difficult tasks. Deep learning’s success largely comes from the ability to use the backpropagation algorithm to efficiently calculate the gradient of an objective function over each model parameter. Neural network models are highly expressive and flexible, and if we are able to find a suitable set of model parameters, we can use neural nets to solve many challenging problems.
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This is the first post in a series of articles, where I plan to show how to apply these algorithms to a range of tasks from MNIST, OpenAI Gym, Roboschool to PyBullet environments. I try to keep the equations light, and I provide links to original articles if the reader wishes to understand more details. In this post I explain how evolution strategies (ES) work with the aid of a few visual examples.