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On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration

Yirui Zhou, Yunfei Jin, Xiaowei Liu, Xiaofeng Zhang, Yangchun Zhang

TL;DR

The policy optimization method in generative adversarial imitation from observation (GAIfO) with distributional soft actor-critic (DSAC) is modified, and the Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE) algorithm is proposed to solve the LfO problem.

Abstract

Learning from observations (LfO) replicates expert behavior without needing access to the expert's actions, making it more practical than learning from demonstrations (LfD) in many real-world scenarios. However, directly applying the on-policy training scheme in LfO worsens the sample inefficiency problem, while employing the traditional off-policy training scheme in LfO magnifies the instability issue. This paper seeks to develop an efficient and stable solution for the LfO problem. Specifically, we begin by exploring the generalization capabilities of both the reward function and policy in LfO, which provides a theoretical foundation for computation. Building on this, we modify the policy optimization method in generative adversarial imitation from observation (GAIfO) with distributional soft actor-critic (DSAC), and propose the Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE) algorithm to solve the LfO problem. MODULE incorporates the advantages of (1) high sample efficiency and training robustness enhancement in soft actor-critic (SAC), and (2) training stability in distributional reinforcement learning (RL). Extensive experiments in MuJoCo environments showcase the superior performance of MODULE over current LfO methods.

On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration

TL;DR

The policy optimization method in generative adversarial imitation from observation (GAIfO) with distributional soft actor-critic (DSAC) is modified, and the Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE) algorithm is proposed to solve the LfO problem.

Abstract

Learning from observations (LfO) replicates expert behavior without needing access to the expert's actions, making it more practical than learning from demonstrations (LfD) in many real-world scenarios. However, directly applying the on-policy training scheme in LfO worsens the sample inefficiency problem, while employing the traditional off-policy training scheme in LfO magnifies the instability issue. This paper seeks to develop an efficient and stable solution for the LfO problem. Specifically, we begin by exploring the generalization capabilities of both the reward function and policy in LfO, which provides a theoretical foundation for computation. Building on this, we modify the policy optimization method in generative adversarial imitation from observation (GAIfO) with distributional soft actor-critic (DSAC), and propose the Mimicking Observations through Distributional Update Learning with adequate Exploration (MODULE) algorithm to solve the LfO problem. MODULE incorporates the advantages of (1) high sample efficiency and training robustness enhancement in soft actor-critic (SAC), and (2) training stability in distributional reinforcement learning (RL). Extensive experiments in MuJoCo environments showcase the superior performance of MODULE over current LfO methods.
Paper Structure (19 sections, 2 theorems, 38 equations, 5 figures, 3 tables)

This paper contains 19 sections, 2 theorems, 38 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Generalization properties for learning from demonstrations
  4. Learning from observations
  5. Preliminaries
  6. Adversarial training in learning from observations
  7. Distributional soft actor-critic
  8. Generalization properties of LfO
  9. Generalization for the reward function
  10. Generalization for the policy
  11. MODULE algorithm
  12. Evaluation
  13. Experimental setup
  14. Comparative experiments
  15. Impact of quantile fraction generation methods and risk measures
  16. ...and 4 more sections

Key Result

Theorem 1

For a uniformly bounded reward function class $\mathcal{R}$ with respect to $s,s'$, i.e., for any $r\in \mathcal{R}$, $\max_{s,s'}|r(s,s')|\leq B_{r}$, and the policy $\pi_{\rm I}$ learned by Eq. lfo_problem satisfies then for all $\delta \in (0,1)$, with probability at least $1-\delta$, we have that

Figures (5)

  • Figure 1: An illustration of the misjudgment by the learned reward: Given $(s,s')$ as the expert's state $s$ and its next state $s'$ in LfO, which corresponds to the expert state-action pair $(s,a_{1})$ in LfD, misjudgment occurs when the agent might execute alternative actions $a_{i}$ ($i=2,\ldots,n$) that have the same transition dynamics leading to $s'$.
  • Figure 2: Comparison of MODULE against advanced LfO methods in three MuJoCo environments.
  • Figure 3: Performance of three quantile fraction generation methods under the MODULE algorithm in three MuJoCo environments.
  • Figure 4: Performance of five risk-averse measure functions, together with the risk-neutral measure function under the MODULE algorithm in three MuJoCo environments.
  • Figure 5: Performance of three risk-seeking measure functions, together with the risk-neutral measure function under the MODULE algorithm in three MuJoCo environments.

Theorems & Definitions (7)

  • Definition 1: LfO reward distance
  • Theorem 1: LfO generalization for the reward function
  • Definition 2: State transition distribution error
  • Remark 1
  • Theorem 2: LfO generalization for the policy
  • proof
  • proof