Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning (1910.11956v1)

Abstract: We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage that produces goal-conditioned hierarchical policies, and a reinforcement learning phase that finetunes these policies for task performance. Our method, while not necessarily perfect at imitation learning, is very amenable to further improvement via environment interaction, allowing it to scale to challenging long-horizon tasks. We simplify the long-horizon policy learning problem by using a novel data-relabeling algorithm for learning goal-conditioned hierarchical policies, where the low-level only acts for a fixed number of steps, regardless of the goal achieved. While we rely on demonstration data to bootstrap policy learning, we do not assume access to demonstrations of every specific tasks that is being solved, and instead leverage unstructured and unsegmented demonstrations of semantically meaningful behaviors that are not only less burdensome to provide, but also can greatly facilitate further improvement using reinforcement learning. We demonstrate the effectiveness of our method on a number of multi-stage, long-horizon manipulation tasks in a challenging kitchen simulation environment. Videos are available at https://relay-policy-learning.github.io/

Relay Policy Learning: A Hierarchical Approach to Long-Horizon Robotic Tasks

The paper "Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning" introduces a novel methodology termed Relay Policy Learning (RPL), which leverages a combination of imitation learning (IL) and reinforcement learning (RL) for tackling complex robotic tasks that require a sequence of actions over an extended period. The paper is grounded in the context of addressing the limitations of traditional hierarchical RL methods, such as issues with exploration and segmentation, and seeks to provide a solution through the usage of unstructured demonstrations.

Approach

The RPL framework is designed to operate in two phases: Relay Imitation Learning (RIL) and Relay Reinforcement Fine-tuning (RRF). RIL serves as the initial phase, where demonstration data is employed to learn goal-conditioned hierarchical policies. This phase uses novel data relabeling algorithms, which allow the model to learn from unstructured, non-segmented demonstrations. This relabeling enables the creation of a dataset that adapts to various potential goals without explicit differentiation of sub-tasks, thereby simplifying the complexity inherent in long-horizon policy learning.

In the RRF phase, policies obtained from RIL are further refined using RL. This phase allows for the fine-tuning of the hierarchical policies by interacting with the environment, which is essential to overcome the potential inadequacies of imitation learning alone, particularly when faced with new and complex task requirements.

Key Contributions

1. Relay Policy Structure: This two-tiered hierarchical architecture comprises a high-level goal-setting policy and a low-level policy for executing actions based on set subgoals. The architecture supports temporal abstraction, facilitating long-term planning and execution.
2. Data Relabeling for Hierarchical Policies: The paper's innovative data relabeling method constructs effective learning datasets from unstructured demonstration data, allowing policies to generalize across numerous tasks without needing carefully segmented inputs.
3. Hierarchical Fine-tuning with Reinforcement Learning: By combining pre-training through imitation learning with reinforcement learning, the RPL framework fine-tunes hierarchical policies more robustly, addressing error compounding issues that often plague pure imitation learning efforts.

Experimental Results

The proposed RPL method was evaluated in a simulated robotic kitchen environment, where the robot performed various complex manipulation tasks. Experimental comparisons with baseline methods, including standard imitation learning and hierarchical RL techniques, demonstrated that RPL significantly outperforms these baselines regarding task completion rates.

Particularly, the paper highlights how RPL's ability to leverage unstructured demonstrations, combined with the hierarchical policy's goal-conditioned reinforcement fine-tuning, enables the robot to manage multi-stage tasks with compelling efficiency. The analysis shows that conventional flat imitation learning strategies are less effective at dealing with long sequences of tasks compared to the hierarchical strategy employed by RPL.

Implications and Future Directions

The insights derived from this research indicate that hierarchical approaches leveraging unstructured demonstrations can greatly enhance the scalability and adaptability of machine learning systems applied to robotics. This method is particularly beneficial in environments where obtaining fully labeled data is impractical. Practically, RPL’s architecture could be extended to physical robot platforms, aiming to diminish the gap between simulated and real-world performance.

Potential future developments include adapting the RPL framework for adoption with off-policy RL algorithms, which would likely improve data efficiency and potentially facilitate real-world deployment. Moreover, exploring generalization capabilities for increasingly complex, unforeseen tasks could underpin significant advances in autonomous systems' operational flexibility.

In summary, the Relay Policy Learning framework offers a substantial advancement in tackling the challenges posed by long-horizon robotic tasks, providing a structured yet flexible approach to policy learning that could be more broadly applied across various domains of artificial intelligence and robotics.