A key challenge in deep reinforcement learning (RL) is the need for large amounts of data. In this post, we present a new algorithm that uses interactive convex optimization to accelerate learning in some deep RL domains. This approach has the potential to improve the speed of training on many real-world tasks, including robotics and video games.
RL is a framework for solving decision-making problems by interacting with an environment. The agent tries to find actions that maximize some notion of cumulative reward, while learning from its environment using trial and error. Deep RL can solve complex tasks, but it often requires a lot of data or computation to learn good policies.
Our approach produces policies that are both efficient and effective. We can train our algorithm on a new domain with less than an hour of interaction—roughly 10 million frames—and achieve state-of-the-art performance on Atari games and 3D robotic manipulation tasks like pushing buttons and stacking blocks.
Interactive convex optimization techniques have been widely used in computer vision, robotics, and other fields to reduce the number of queries required for learning from humans . Here we demonstrate how interactive methods can be used within the context of reinforcement learning too.
Deep reinforcement learning helps agents learn to make good decisions. Most of its applications use neural networks to represent the value function or the policy and apply some form of stochastic gradient descent (SGD) for optimization. This can lead to a lot of wasted computation because many parameters are updated in the wrong direction before reaching an optimum. Interactive methods like Mirror Descent can be used instead, allowing an expert to provide feedback that improves optimization speed and data efficiency.
The OpenAI Gym provides an environment where different algorithms can be tested and compared. In this post, we review the basics of reinforcement learning and deep reinforcement learning, implement a simple state-of-the-art deep RL algorithm using interactive convex optimization, and compare the performance on two environments from the gym.
In the past several years, reinforcement learning has achieved many successes in a variety of tasks. However, research on deep reinforcement learning is still in its infancy. Some critical challenges remain to be solved before it can be widely used. In this paper, we present a new deep reinforcement learning algorithm based on interactive convex optimization (ICO). This approach has two main advantages. First, it provides a novel way of accelerating deep reinforcement learning with various methods developed in convex optimization. Second, it allows us to handle nonconvex problems by alternating between convex approximation and policy search.
Deep reinforcement learning is a natural candidate for policy search with value function approximation. This problem was introduced by Doya (2000) and Sutton et al. (2014) for the case where the approximate value function is linear in the policy parameters, and by Kakade (2002) for the case where the approximate value function is nonlinear in the policy parameters.
In this paper, we propose a new algorithm for solving deep reinforcement learning problems with ICO—a policy search method based on convex optimization—and show that it has better stability and performance than existing approaches. Our algorithm alternates between optimizing an approximate value function over convex approximations of the state space and updating the policy based on
Algorithms for deep reinforcement learning (RL) often require many trials to converge, and thus are expensive to run. In this post, we introduce a new extension of the policy gradient theorem for reinforcement learning with convex function approximation that can be used to make these algorithms interactive.
Rather than following a long trajectory through the state-action space and using an estimate of the gradient computed from this trajectory to update the parameters at each iteration, we can use our knowledge of what is a good update direction in each state to make updates after much shorter trajectories.
In particular, we show how to extend the policy gradient theorem so that it applies when we have access to an oracle that returns a feasible solution to convex optimization problems arising from RL. We use this extended theorem in conjunction with cutting plane methods to develop an algorithm that can be applied when the parameter space is a convex set and the state-action value function can be represented by a neural network with rectified linear units (ReLUs). This algorithm requires only maintaining an on-policy dataset, which makes it fast and memory efficient. We experimentally demonstrate its effectiveness in two domains: Atari 2600 games and continuous control tasks drawn from the OpenAI Gym Benchmark.
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The OpenAI Codex is a text-based adventure game that machines can play. The Codex has been designed to test and develop AI capabilities, like intelligent assistants and smart robots, that can understand and interact with language. For example, a machine in the Codex must be able to learn from its past experiences to solve problems, develop strategies, and explore new worlds.
This is the first release of the OpenAI Codex. We welcome you to contribute your own ideas for interactive computer games. The Codex will help us build more sophisticated systems for playing text-based adventure games, which are an important area for AI research and development.
The Codex is designed to be easy to use and understand, so it can be played by people of all skill levels and ages. We’ve also made it easy to develop your own games using the open source code available on GitHub. We hope you have fun playing the OpenAI Codex!