Learning by Playing - Solving Sparse Reward Tasks from Scratch (1802.10567v1)

Abstract: We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach.

• The paper introduces SAC-X to overcome sparse rewards by integrating auxiliary tasks for enhanced exploration.
• It employs multi-objective, off-policy learning with a high-level scheduler that optimizes multiple intention policies concurrently.
• Experimental results demonstrate SAC-X’s superior sample efficiency and robustness, enabling effective learning on complex robotic tasks.

Learning by Playing: An Analysis of SAC-X and Its Implications

The paper "Learning by Playing -- Solving Sparse Reward Tasks from Scratch" presents the concept of Scheduled Auxiliary Control (SAC-X), a novel reinforcement learning framework designed to address the challenges associated with sparse reward problems. Sparse rewards, which occur infrequently or in specific segments of the state space, are commonly encountered in complex robotic tasks such as manipulation or navigation. Traditional reinforcement learning models face difficulties in efficiently exploring such environments due to the low probability of encountering meaningful reward signals through random exploration alone.

Core Contributions of SAC-X

The proposed SAC-X framework introduces a structure where an agent is guided by multiple auxiliary tasks alongside the primary task. This setup fosters enhanced exploration and learning in environments with sparse reward signals. The auxiliary tasks, which are easier to define and compute, provide additional reward structures that enable the agent to explore its state-action space more effectively. The key aspects of this approach include:

1. Multi-Objective Learning: Each state-action pair is associated with multiple reward signals, including both external (primary task) and auxiliary rewards. The agent learns distinct intention policies for each reward type, aiming to optimize the cumulative reward for these auxiliary objectives.
2. Scheduled Exploration: A high-level scheduler dynamically selects and executes these intention policies throughout exploration, thereby focusing the agent's behavior towards environments or actions that are informative for sparse main tasks.
3. Off-Policy Learning: The framework utilizes off-policy reinforcement learning, allowing simultaneous updates across different policies and sharing experiences across intentions to enhance learning efficiency.
4. Robustness and Transfer: SAC-X is demonstrated to be highly sample efficient and capable of transferring learned behaviors across different but related tasks. This property is crucial for real-world robotic applications, where retraining from scratch for each new task is impractical.

Experimental Evidence and Benchmarking

The paper provides empirical validation of SAC-X across multiple robotic manipulation tasks, both in simulation and on real hardware. Notable experiments include block stacking, manipulation of objects with complex shapes, and computationally intensive cleanup tasks involving multiple objects. Some of the critical findings are:

• Task Performance: SAC-X consistently outperforms traditional reinforcement learning approaches like DDPG and variations that do not use scheduling, such as IUA. The proposed method also shows superior performance in terms of sample efficiency and learning speed.
• Generality of Learning: SAC-X can handle variations in task dynamics and object shapes without the need for significant redesigns in the reward structure. For instance, the same auxiliary tasks facilitate the learning of different stacking configurations.
• Effectiveness in Real-World Settings: Experiments conducted with a real Kinova Jaco robotic arm indicate that SAC-X is not only theoretical but also practically viable. The system learned sophisticated manipulation tasks from scratch in a reasonable timeframe, demonstrating robustness to real-world uncertainties.

Implications and Future Directions

The primary contribution of SAC-X lies in its structured exploitation of auxiliary tasks to mitigate the exploration challenges posed by sparse rewards. The approach aligns closely with observational learning methods in humans, where complex behaviors are learned from intermediate milestones rather than singular objectives.

Theoretical Implications: The SAC-X framework enriches the theoretical groundwork of hierarchical reinforcement learning by showcasing the benefits of intentional exploration strategies. This could inspire further research into dynamically adaptive hierarchical models, balancing exploration and exploitation more effectively.

Practical Implications: In robotics, SAC-X can significantly reduce the time and human intervention required to program robots for tasks where direct reward signals are sparse or difficult to specify. It opens up possibilities for deploying robots in unstructured environments, autonomously learning tasks that involve interaction with numerous objects or variables.

Future Research: The extension of SAC-X to broader domains and its integration with other machine learning paradigms such as transfer learning and meta-learning could lead to robust systems capable of quickly adapting to a wide range of situations. Additionally, exploring the optimization of the scheduler’s learning process may yield further improvements in efficiency and adaptability.

In summary, the SAC-X framework represents a substantial advancement in reinforcement learning for tasks characterized by sparse rewards. By leveraging auxiliary tasks and learned scheduling, it sets a new precedent for both theoretical exploration and practical application in robotic learning.