Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model (1907.00953v4)

Abstract: Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these high-dimensional observation spaces present a number of challenges in practice, since the policy must now solve two problems: representation learning and task learning. In this work, we tackle these two problems separately, by explicitly learning latent representations that can accelerate reinforcement learning from images. We propose the stochastic latent actor-critic (SLAC) algorithm: a sample-efficient and high-performing RL algorithm for learning policies for complex continuous control tasks directly from high-dimensional image inputs. SLAC provides a novel and principled approach for unifying stochastic sequential models and RL into a single method, by learning a compact latent representation and then performing RL in the model's learned latent space. Our experimental evaluation demonstrates that our method outperforms both model-free and model-based alternatives in terms of final performance and sample efficiency, on a range of difficult image-based control tasks. Our code and videos of our results are available at our website.

Analysis of Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

The paper "Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model" introduces the Stochastic Latent Actor-Critic (SLAC) algorithm, which effectively addresses the challenge of learning policies from high-dimensional observations in deep reinforcement learning (RL). The core of this work lies in its novel integration of representation learning through a stochastic latent variable model with the task learning capabilities of deep RL, thereby achieving enhanced sample efficiency and performance.

Overview and Methodological Insights

SLAC stands out by distinctly separating representation learning from task learning. This separation alleviates the typical inefficiencies encountered when solving the dual challenge of representation and task learning simultaneously in high-dimensional image input scenarios. To achieve this, the authors leverage a stochastic latent actor-critic framework that allows RL on a latent space model. Here, a compact latent representation is learned explicitly, which serves as the foundation for RL, unlike model-free algorithms that learn end-to-end from raw observations.

The algorithm stands on the construction of a partially observed Markov decision process (POMDP), where the stochastic latent state aids in encoding uncertainties between state variables. SLAC utilizes a variational inference approach to derive its latent model, which is both computationally manageable and effective. As a result, it provides a novel approximation for policy learning that sidesteps the complexities of solving the full POMDP problem, maintaining ease of training and stability in practice.

Empirical Evaluation and Results

The experimental results demonstrate that SLAC outperforms existing alternatives, both model-based and model-free, in terms of sample efficiency and final performance. Using benchmarks from the DeepMind Control Suite and OpenAI Gym, SLAC achieves comparable or superior performance to state-of-the-art methods, such as SAC and PlaNet, indicating its high viability for complex image-based control tasks.

One of the prominent numerical results highlighted in the paper is SLAC's ability to rapidly converge to competent policies and outperform PlaNet on tasks like Cheetah Run and Walker Walk. The algorithm's capability to efficiently manage representation learning enables it to approach the upper performance limits traditionally accessible only when learning from state inputs, corroborated by the authors' ablation studies which confirm the advantages of their chosen model structure.

Implications and Future Directions in AI

The integration of stochastic latent variable models with actor-critic architectures in SLAC sets a promising direction for RL methodologies dealing with high-dimensional inputs. The successful decoupling of representation and task learning has potential repercussions for not only advancing RL strategies but also improving the generalization capabilities of AI models across varied tasks and environments.

SLAC paves the way for further explorations into adaptive model architectures that can exploit latent representations efficiently, potentially minimizing the dependency on extensive interaction data traditionally required for training such systems. Future research could refine these approaches by incorporating elements like online adaptation, meta-learning for representation transfer, and hybrid architectures leveraging the robustness of model-based rollouts.

In conclusion, SLAC provides a framework that offers a substantial improvement in the scalability and efficiency of RL for intricate tasks, marking a significant step forward in the application of AI to real-world scenarios. The methodology not only elevates performance on existing benchmarks but also enriches the theoretical understanding and practical application of latent models in reinforcement learning.