Visual Reinforcement Learning with Imagined Goals (1807.04742v2)

Abstract: For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general-purpose skills by combining unsupervised representation learning and reinforcement learning of goal-conditioned policies. Since the particular goals that might be required at test-time are not known in advance, the agent performs a self-supervised "practice" phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self-supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample-efficiency of our method. Our off-policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real-world robotic system, and substantially outperforms prior techniques.

Visual Reinforcement Learning with Imagined Goals: An Expert Overview

The paper "Visual Reinforcement Learning with Imagined Goals" proposes a novel framework to facilitate autonomous agents, like robots, in learning versatile skills through reinforcement learning (RL) from raw sensory inputs, specifically images. This approach highlights the integration of unsupervised representation learning with RL to develop goal-conditioned policies, allowing agents to self-generate goals and learn from these imagined targets.

Key Contributions and Findings

The proposed method, termed as Reinforcement Learning with Imagined Goals (RIG), introduces the combination of variational autoencoders (VAEs) with RL, employing a multi-faceted utility of VAEs:

1. Structured Representation: The VAE model generates a latent space that is a structured representation of raw sensory data. This latent space simplifies the training of policies operating directly on image input, facilitating more efficient learning even in complex environments.
2. Goal Sampling for Exploration: By sampling from the learned latent distribution, agents can autonomously set and practice reaching diverse goals, promoting a richer exploration without human intervention.
3. Reward Shaping: The proposed system leverages the latent space to redefine the reward function, utilizing distances that are more meaningful and well-shaped compared to pixel-based Euclidean distances.
4. Goal Relabeling: RIG introduces a retroactive goal relabeling mechanism which aids in sample efficiency. By resampling goals from the learned latent space, the algorithm enhances the diversity and quantity of training data, improving performance over traditional methods like Hindsight Experience Replay (HER).

Numerical Results and Impact

Extensive experiments in simulated environments, such as visual reaching, pushing, and multi-object tasks, demonstrate the notable success of RIG in successfully reaching specified visual goals. The framework performs comparably to or exceeds baseline methods, despite the challenges inherent in perception and control from image data. RIG also highlights significant improvements in real-world tasks, showing its applicability beyond simulation with only modest real-world interaction requirements.

Theoretical and Practical Implications

From a theoretical perspective, the success of RIG illustrates the potential for integrating generative models into RL to address the challenges of representation and reward specification when dealing with high-dimensional observation spaces like images. Practically, this provides a pathway toward developing general-purpose robotic agents capable of executing a breadth of tasks defined by visual goals, without needing complex instrumentation or manual reward engineering.

Future Directions in Visual RL

The paper opens avenues for future work through several intriguing possibilities:

• Enhanced Exploration Strategies: Combining intrinsic motivation approaches with goal sampling could further optimize exploration, thus enhancing learning efficiency in unknown environments.
• Multitask and Meta-Learning: Given its generality, RIG could serve as a foundation for policies that adapt to multiple or meta-tasks, potentially learning across diverse environments with minimal retraining.
• Human-Readable Goal Specifications: Extending the goal representation from visual inputs to more abstract forms such as language or demonstrations could result in more intuitive human-agent interfaces.

In summary, the paper delineates a compelling approach to visual RL by intelligently leveraging unsupervised learning techniques within reinforcement frameworks. This work stands as an impactful step towards more autonomous, adaptable learning systems in both virtual and real-world settings.