Generalizing Inverse Kinematics for Representation Learning to Finite Memory Partially Observable Markov Decision Processes

Core Concepts

This work generalizes inverse kinematics techniques to learn agent-centric state representations in high-dimensional, finite-memory Partially Observable Markov Decision Processes (FM-POMDPs).

Abstract

The paper addresses the challenge of learning informative, agent-centric state representations in high-dimensional, non-Markovian environments, where the current observation alone is insufficient to decode the relevant state.
The key contributions are:

Establishing that naive generalizations of inverse kinematics to the FM-POMDP setting can fail, both theoretically and empirically.

Proposing a generalized inverse model, Masked Inverse Kinematics with Actions (MIK+A), that can provably recover the agent-centric state representation under assumptions of past and future decodability.

Providing a thorough empirical evaluation of the proposed objectives on partially observable navigation tasks, demonstrating the superiority of MIK+A in recovering the agent-centric state.

Showing the effectiveness of the proposed objectives in learning useful representations for visual offline reinforcement learning tasks with partial observability.

The paper establishes that MIK+A, which leverages both past and future observation sequences along with action information, is the most effective approach for discovering the complete agent-centric state representation in FM-POMDPs.

Stats

"The agent-centric dynamics are deterministic and the diameter of the control-endogenous part of the state space is bounded."
"We assume our action space is finite and our agent-centric state space is also finite."

Quotes

"Discovering an informative, or agent-centric, state representation that encodes only the relevant information while discarding the irrelevant is a key challenge towards scaling reinforcement learning algorithms and efficiently applying them to downstream tasks."
"We establish that generalized inverse models can be adapted for learning agent-centric state representation for this task."
"Our results show that our variant of the multi-step inverse model (Lamb et al., 2022) can indeed succeed in the FM-POMDP setting."

Key Insights Distilled From

Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs

by Lili Wu,Ben ... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14552.pdf

Deeper Inquiries

How can the proposed techniques be extended to handle continuous state and action spaces in FM-POMDPs

To extend the proposed techniques to handle continuous state and action spaces in FM-POMDPs, we can leverage function approximation methods such as neural networks. By using neural networks to represent the encoder and decoder functions, we can handle continuous state and action spaces effectively. The neural networks can learn to map continuous observations to a latent representation and decode the latent representation back to the original state space. Additionally, techniques like autoencoders or variational autoencoders can be used to learn a continuous latent representation of the state space. Reinforcement learning algorithms can then be applied to learn the optimal policy in the latent space.

What are the limitations of the past and future decodability assumptions, and how can they be relaxed or replaced with alternative structural assumptions

The past and future decodability assumptions have limitations in scenarios where the state transitions are complex and not easily decodable from a fixed number of past or future observations. These assumptions may not hold in environments with long-term dependencies or where the state is influenced by a large number of past observations. To relax or replace these assumptions, alternative structural assumptions can be considered. For example, incorporating memory mechanisms like LSTM or Transformer models can help capture long-term dependencies in the observations. Additionally, techniques like attention mechanisms can be used to focus on relevant parts of the past and future observations for state decoding. By incorporating more flexible and adaptive models, we can relax the strict past and future decodability assumptions and improve the robustness of the agent-centric state discovery process.

Can the insights from this work be applied to develop online RL algorithms that actively discover the agent-centric state representation in FM-POMDPs

The insights from this work can be applied to develop online RL algorithms that actively discover the agent-centric state representation in FM-POMDPs by incorporating the multi-step inverse kinematics objectives into the learning process. Online RL algorithms can use these objectives as auxiliary tasks to guide the representation learning process while interacting with the environment. By continuously updating the agent-centric state representation based on past and future observations, the algorithm can adapt to changing dynamics and discover the relevant information for decision-making. Additionally, techniques like online sequence modeling and reinforcement learning can be used to learn the latent state representation in real-time. By integrating the insights from this work into online RL algorithms, we can develop efficient and adaptive agents that actively discover the agent-centric state in FM-POMDPs.