Goal-conditioned Imitation Learning (1906.05838v3)

Abstract: Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation. Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical. Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward. Unfortunately, without tricks like resetting to points along the trajectory, HER might require many samples to discover how to reach certain areas of the state-space. In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms. Furthermore, we show our method can also be used when the available expert trajectories do not contain the actions, which can leverage kinesthetic or third person demonstration. The code is available at https://sites.google.com/view/goalconditioned-il/.

• The paper introduces goalGAIL, which fuses HER and GAIL to enhance sample efficiency using state-only and suboptimal demonstrations.
• It employs an expert relabeling technique to augment data without extra supervision, addressing sparse reward challenges in robotics.
• Experimental results show that goalGAIL converges faster than standard HER and GAIL, achieving robust performance in simulated robotic environments.

Overview of "Goal-conditioned Imitation Learning"

The paper "Goal-conditioned Imitation Learning" investigates the challenges and advancements in applying Goal-conditioned Reinforcement Learning (RL) in robotics. The authors address the practical issues of reward design by leveraging a combination of Hindsight Experience Replay (HER) and Imitation Learning (IL) to enhance the efficiency and generalizability of learning goal-conditioned policies. This research presents goalGAIL, an algorithm that integrates Generative Adversarial Imitation Learning (GAIL) with HER to accelerate learning and broaden applicability, especially when state-only or suboptimal demonstrations are available.

The fundamental problem of reward design in RL for robotics is highlighted by the paper, focusing on the inherent difficulties such as the need for extensive supervision and the impracticality of multiple reward setups for diverse tasks. The authors emphasize the potential of self-supervised approaches that do not rely on explicit reward functions but instead use goal-conditioned learning paradigms. In such paradigms, the policy is trained to reach any arbitrary state upon request, formulated as goals. The paper critiques methods like HER that, while effective, may suffer from inefficiency in exploring complex state-spaces without external guidance.

Contributions and Methodology

1. Introduction of goalGAIL:
   • The algorithm goalGAIL combines the strengths of HER and GAIL, allowing accelerated learning of policies in environments with sparse rewards or complex state-space topologies. By incorporating adversarial training, the approach can use available demonstration data to speed up convergence and improve sample efficiency, even when expert trajectories lack action information or are noisy.
2. Expert Relabeling Technique:
   • This novel technique augments the data for learning by considering transitions within expert demonstrations as valid experiences for alternative goals. This effectively broadens the dataset without needing additional expert interactions, especially beneficial in low-data scenarios often encountered in practical robotics applications.
3. State-only Demonstrations:
   • The paper extends the utility of IL by showing that goalGAIL can operate successfully with state-only demonstrations, bypassing the need for access to expert actions. This approach leverages the discriminator in GAIL to assess the transition quality based on endpoint states, facilitating learning from third-person or kinesthetic demonstrations.
4. Sub-optimal Expert Robustness:
   • Unlike traditional IL methods which might degrade with suboptimal demonstrations, goalGAIL shows robustness by maintaining policy performance. The adversarial framework adapts to demonstration noise, potentially using it as a resolution factor in policy differentiation.

Results

The experimental results across several simulated robotic environments show that goalGAIL significantly outperforms standard HER and pure GAIL implementations. With the use of demonstrations, goalGAIL achieves faster convergence to effective policies that can outperform the original expert demonstrations. The expert relabeling technique is validated as an effective augmentation for improving the learning process in restrictive conditions.

Implications and Future Direction

Practically, the framework developed in this paper broadens the horizon for robotic applications, where the crafting of explicit reward functions is either infeasible or inefficient. It paves a path towards more autonomous systems capable of learning from limited data with minimal human intervention. Theoretically, this research contributes to understanding the integration of adversarial and goal-conditioned learning paradigms, highlighting the importance of data efficiency and adaptability in RL.

Moving forward, there are potential expansions into real-world applications, particularly those using sensory data like vision. The applicability of goalGAIL in high-dimensional input spaces represents an exciting avenue for future studies, especially considering the challenges of transferring these methodologies to real-world, sensor-heavy environments.

In conclusion, "Goal-conditioned Imitation Learning" offers a substantive step forward in addressing the structural and practical limitations of traditional RL in robotics, promoting a symbiotic use of imitation and self-supervised learning paradigms to achieve robust and efficient robotic policies.