From Pixels to Torques: Policy Learning with Deep Dynamical Models (1502.02251v3)

Abstract: Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model that uses deep auto-encoders to learn a low-dimensional embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning ensures that not only static but also dynamic properties of the data are accounted for. This is crucial for long-term predictions, which lie at the core of the adaptive model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art reinforcement learning methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces and is an important step toward fully autonomous learning from pixels to torques.

• The paper introduces a deep dynamical model that learns low-dimensional embeddings from pixel observations to enable effective closed-loop control.
• It employs a joint learning approach that integrates feature extraction with dynamic modeling, improving long-term predictive accuracy.
• Adaptive model predictive control is utilized to iteratively refine policies, achieving performance comparable to methods using true state information.

Summary of "From Pixels to Torques: Policy Learning with Deep Dynamical Models"

The paper "From Pixels to Torques: Policy Learning with Deep Dynamical Models" addresses the challenge of developing data-efficient model-based reinforcement learning (RL) algorithms for learning control policies in environments defined by continuous state-action spaces, using high-dimensional observations such as images. The central focus of this research is the "pixels to torques" problem, wherein an agent must learn to perform closed-loop control using only pixel data from its environment, with no direct access to the underlying state information of the system.

Key Contributions

1. Deep Dynamical Model (DDM): The authors introduce a novel DDM that effectively learns a low-dimensional embedding of high-dimensional image observations using deep auto-encoders. This model simultaneously learns a predictive transition model in the feature space, capturing both static and dynamic properties necessary for long-term predictions crucial for adaptive model predictive control (MPC).
2. Joint Learning Approach: Emphasizing the significance of joint learning, the paper describes a methodology where feature extraction and dynamic modeling are integrated. This ensures that the learned features are suitable for predicting dynamics, not just for preserving reconstruction quality.
3. Adaptive Model Predictive Control (MPC): With the learned DDM, the authors integrate MPC to dynamically adapt to new observations and improve the model iteratively across trials. This approach is proposed as an effective way to handle the complexity and dimensionality of environments where only pixel data are available.

Experimental Analysis

The empirical assessments involved training and testing the DDM on synthetic image data representing control tasks such as moving a pendulum to a target position. Multiple experiments highlight the efficacy of the joint learning in producing compact and dynamic feature representations which facilitate reliable long-term predictions. Notably, after sufficient trials, the learned control policies using DDM exhibited a success rate approaching comparable methods applied with the true underlying state, thus verifying its potential in high-dimensional control tasks. The paper also compared the proposed method against PILCO, a state-of-the-art data-efficient model-based RL method, and demonstrated favorable results in terms of computational efficiency and control performance.

Implications and Future Directions

The implications of this research are substantial in the field of autonomous systems and robotics, where fully autonomous learning using high-dimensional sensor data is pivotal. By eschewing reliance on human-engineered features and directly utilizing pixel data, the proposed methodology makes strides towards realizing genuinely autonomous agents capable of learning intricate closed-loop control policies in complex environments.

The theoretical foundation laid by deep dynamical models encourages further exploration into scalable RL algorithms that can integrate richer sensory modalities beyond pixels. Future work could focus on refining joint learning algorithms to enhance versatility across diverse robotic tasks and improving computational efficiencies to facilitate real-time applications.

In conclusion, the paper promises notable advancements in the ability of AI systems to autonomously learn from high-dimensional observations, presenting a step towards bridging perceptual data processing and decision-making in vision-based robotic control settings. The integration of deep learning with dynamical systems modeling offers a promising avenue for addressing complexities inherent in continuous control domains.