Deep Variational Reinforcement Learning for POMDPs (1806.02426v1)

Abstract: Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.

• The paper introduces Deep Variational Reinforcement Learning (DVRL), a framework that combines RL and variational inference to learn a generative model of the environment for improved decision-making under partial observability.
• DVRL demonstrated superior performance over existing RNN-based methods in experiments, particularly in challenging environments like flickering Atari games with high-dimensional partial observations.
• This approach offers a principled way to integrate generative modeling with RL policy learning, opening avenues for applications in robotics and autonomous systems operating under uncertainty.

An Analysis of "Deep Variational Reinforcement Learning for POMDPs"

The paper entitled "Deep Variational Reinforcement Learning for POMDPs" presents a novel framework for addressing Partially Observable Markov Decision Processes (POMDPs) in reinforcement learning (RL). This paper focuses on a substantial challenge in developing reinforcement learning algorithms: managing incomplete and noisy observations from environments whose model is unknown. By introducing a method known as Deep Variational Reinforcement Learning (DVRL), the authors aim to create a learning paradigm that facilitates more accurate inference and decision-making through the use of learned generative models.

Overview of DVRL Method

DVRL incorporates a variational approach in its architecture, incentivizing an agent to learn a generative model of the environment. This model serves as a tool for inference, assisting the aggregation of the sparse data available due to partial observability. The central contribution of the DVRL method is its n-step approximation to the evidence lower bound (ELBO), which allows for the joint training of the generative model and the policy network. This formulation ensures the latent state representation is attuned to the demands of control tasks, supporting a principled update of the belief state which the policy can leverage effectively.

Experimental Validation

The performance of the DVRL approach was evaluated in both the Mountain Hike task and various modified Atari games with flickering conditions to simulate partial observability. In these experiments, DVRL demonstrated superior performance compared to previous RNN-based methods, particularly in environments with high-dimensional observations and complex structures, such as the flickering Atari games.

Implications and Future Directions

The implications of DVRL span both theoretical and practical landscapes. Theoretically, DVRL bridges RL policy learning with variational inference techniques, introducing a structured manner of combining generative modeling with discriminative tasks. Practically, this method could be applied across domains where sensor noise and occlusions are common, such as robotics and autonomous navigation. The incorporation of a belief state computation aligns DVRL's objectives with those in robust policy learning and reliable decision-making under uncertainty.

The DVRL framework opens numerous avenues for future research. Here are a few notable paths:

1. Enhanced Model Architectures: Exploration into more expressive neural architectures could further enhance the capacity of the DVRL framework to capture complex belief states.
2. Robustness in High Dimensional Spaces: Extending the approach to handle even higher-dimensional observation spaces or more complex real-world scenarios.
3. Integration with Curiosity-Driven Exploration: Given the emphasis on model learning, DVRL could be expanded to include curiosity-driven exploration, leveraging model uncertainty to guide exploration.
4. Application to Multi-Agent Systems: Extending DVRL to multi-agent systems could have significant implications in environments where agents share partial observations and need to coordinate.

In conclusion, the paper "Deep Variational Reinforcement Learning for POMDPs" delineates an innovative intersection between variational inference and reinforcement learning, encouraging the development of more interpretable and structure-aware RL models. This approach poses significant advancements in managing the intrinsic challenges encountered in environments characterized by incomplete and noisy sensory data.