Learning to Reach Goals via Iterated Supervised Learning (1912.06088v4)

Abstract: Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study RL algorithms that use imitation learning to acquire goal reaching policies from scratch, without the need for expert demonstrations or a value function. In lieu of demonstrations, we leverage the property that any trajectory is a successful demonstration for reaching the final state in that same trajectory. We propose a simple algorithm in which an agent continually relabels and imitates the trajectories it generates to progressively learn goal-reaching behaviors from scratch. Each iteration, the agent collects new trajectories using the latest policy, and maximizes the likelihood of the actions along these trajectories under the goal that was actually reached, so as to improve the policy. We formally show that this iterated supervised learning procedure optimizes a bound on the RL objective, derive performance bounds of the learned policy, and empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms in several benchmark tasks.

• The paper introduces GCSL, which iteratively repurposes agent trajectories as goal demonstrations to optimize reinforcement learning without relying on expert data.
• It replaces complex value function estimation with supervised learning, enhancing stability and reducing sensitivity to hyperparameters in sparse-reward tasks.
• Empirical evaluations across robotics and simulation benchmarks demonstrate that GCSL can match or outperform established methods like TD3, HER, and PPO.

An Overview of "Learning to Reach Goals via Iterated Supervised Learning"

The paper "Learning to Reach Goals via Iterated Supervised Learning" introduces a novel approach to reinforcement learning (RL) focused on achieving goal-reaching behaviors, particularly in scenarios with sparse rewards. Reinforcement learning has advanced in many areas through the use of neural networks, yet it often struggles with issues related to sensitivity to hyperparameters, stability, and optimization, especially when exploring tasks with sparse reward structures. This paper proposes leveraging imitation learning, a typically stable and robust alternative, to address these challenges without depending on expert demonstrations or value function approximations.

Core Contributions

The authors propose a method called Goal-Conditioned Supervised Learning (GCSL). The central insight of GCSL is to iteratively treat trajectories generated by a partially trained policy as successful demonstrations for the goal states actually visited during those trajectories. Rather than needing optimal expert trajectories, any trajectory ending at a goal state can serve as successful training data for reaching that goal. This transforms suboptimal agent behavior into effective domain knowledge, utilized without learning a traditional value function. The process unfolds iteratively wherein newly collected trajectories are "relabeled" with the goals they reach, and the likelihood of achieving these goals is maximized through supervised learning.

Theoretical and Empirical Foundations

The paper provides a rigorous theoretical analysis, establishing that GCSL optimizes a lower bound on the traditional RL objective. Significantly, the work demonstrates that iteratively imitating all the data, even when generated by a suboptimal policy, can still lead the agent towards optimal behaviors. The authors use both theoretical performance bounds and empirical evaluations to support their claims. Experiments across a range of benchmarks — from robotic pushing tasks to the classic Lunar Lander — illustrate that GCSL often surpasses or matches the performance of established RL methods like TD3 with Hindsight Experience Replay (HER) and Proximal Policy Optimization (PPO).

Implications and Future Directions

GCSL distinguishes itself with reduced sensitivity to hyperparameters and by omitting the learning of complex value functions, facilitating ease of use and stability. The insight that goals reached in past trajectories can safely train future policies contributes to a body of work enhancing the stability and practicality of reinforcement learning in sparse-reward settings. It opens avenues for future development in RL, where imitation learning principles can guide policy search without conventional limitations tied to demonstration scarcity or reward function engineering.

Moreover, the capability to integrate earlier mentioned expert or demonstration data effectively suggests GCSL could enrich existing policy initialization methods. Future work could refine exploration strategies within this framework—potentially integrating novelty-seeking approaches—without compromising the simplicity and robustness that GCSL offers.

Conclusion

This paper makes a substantial contribution to the development of RL methodologies that benefit from the stability and robustness of supervised learning, challenging conventional notions that optimal demonstrations are a prerequisite for effective learning. By reframing trajectories as goal-reaching demonstrations, it not only offers an elegant solution for training goal-conditioned policies but also sets the stage for advancements in how RL agents can learn efficiently from their own experiences.