To select effective actions in complex environments, intelligent agents need to generalize from past experience. World models can represent knowledge about the environment to facilitate such generalization. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks purely by latent imagination. We efficiently learn behaviors by backpropagating analytic gradients of learned state values through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

/pdf/dream-to-control-learning-behaviors-by-latent-imagination-2qbweh40ow.pdf

Dream to Control: Learning Behaviors by Latent Imagination

Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly? Part of the answer may be that people can learn how the game works and predict which actions will lead to desirable outcomes. In this paper, we explore how video prediction models can similarly enable agents to solve Atari games with fewer interactions than model-free methods. We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment, which corresponds to two hours of real-time play. In most games SimPLe outperforms state-of-the-art model-free algorithms, in some games by over an order of magnitude.

/pdf/model-based-reinforcement-learning-for-atari-3xtcq1mkpb.pdf

Model Based Reinforcement Learning for Atari

Model-Based Reinforcement Learning for Atari

Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

Dream to Control: Learning Behaviors by Latent Imagination.

In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using state representation learning. The output of this lightweight module is then directly fed into a target-reaching and obstacle-avoiding policy trained with reinforcement learning. We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes. The key part is the state representation, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. In our experiments with the quadrupedal robot ANYmal in simulation and in reality, we show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.

/pdf/learning-a-state-representation-and-navigation-in-cluttered-133og36pld.pdf

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturing the dynamics of scene changes conditioned on robot actions. Our dreaming model can emulate samples equivalent to a sequence of images from the actual environment, technically by learning an action-conditioned future representation/scene regressor. This allows the agent to learn action policies (i.e., visuomotor policies) by interacting with the dreaming model rather than the real-world. We experimentally confirm that our dreaming model enables robot learning of policies that transfer to the real-world.

Learning Real-World Robot Policies by Dreaming

We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a future scene prediction model solely on a collection of expert trajectories consisting of unlabeled example videos and actions, and by enabling generalized action cloning using future image similarity. The robot learns to visually predict the consequences of taking an action, and obtains the policy by evaluating how similar the predicted future image is to an expert image. We develop a stochastic action-conditioned convolutional autoencoder, and present how we take advantage of future images for robot learning. We conduct experiments in simulated and real-life environments using a ground mobility robot with and without obstacles, and compare our models to multiple baseline methods.

/pdf/model-based-behavioral-cloning-with-future-image-similarity-1v43d0ixl5.pdf

Model-based Behavioral Cloning with Future Image Similarity Learning.

We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a future scene prediction model solely from a collection of expert trajectories consisting of unlabeled example videos and actions, and by enabling action selection using future image similarity. In this approach, the robot learns to visually imagine the consequences of taking an action, and obtains the policy by evaluating how similar the predicted future image is to an expert sample. We develop an action-conditioned convolutional autoencoder, and present how we take advantage of future images for zero-online-trial imitation learning. We conduct experiments in simulated and real-life environments using a ground mobility robot with and without obstacles in reaching target objects. We explicitly compare our models to multiple baseline methods requiring only offline samples. The results confirm that our proposed methods perform superior to previous methods, including 1.5 $$\times $$ and 2.5 $$\times $$ higher success rate in two different tasks than behavioral cloning.

Model-Based Robot Imitation with Future Image Similarity

Based on what is seen (i.e. visual input), humans are able to visually predict (i.e. regress) what the scene will look like after taking a certain action. Further, humans are able to take advantage of such predictions to select optimal actions for the task they are working on. Using example videos, robots can also learn to visually imagine the future consequence of taking an action. This can be viewed as learning a function mapping a raw image frame (conditioned on a particular action) to the future image frame. Once learned, the future regression function can be combined with an action policy learning framework (e.g. reinforcement or imitation learning), enabling better robot action learning for given tasks.

/pdf/action-conditioned-convolutional-future-regression-models-1qi4z39yde.pdf

Action-Conditioned Convolutional Future Regression Models for Robot Imitation Learning

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Papers

Learning Real-World Robot Policies by Dreaming

Learning Real-World Robot Policies by Dreaming

Model-based Behavioral Cloning with Future Image Similarity Learning.

Model-Based Robot Imitation with Future Image Similarity

Action-Conditioned Convolutional Future Regression Models for Robot Imitation Learning