Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (1811.01848v3)

Abstract: We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex simulated control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.

• The paper presents a novel approach that merges local trajectory optimization with value function learning to enhance model-based control.
• The paper employs Model Predictive Control for short-horizon planning that mitigates error propagation and accelerates learning in high-dimensional tasks.
• The paper achieves robust exploration through temporally coordinated optimized trajectories, yielding superior state-space coverage in complex environments.

An Analysis of "Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control"

The paper "Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control" proposes an innovative framework, POLO, for agents operating in environments where they must act and learn simultaneously. The work explores the symbiotic relationship between local model-based control, value function learning, and agent exploration, presenting a cohesive approach that integrates trajectory optimization with value function approximation to tackle complex control tasks efficiently.

Core Contributions and Methodology

The POLO framework offers a structured method for continuous learning and acting, blending several key components explicitly designed to enhance model-based control systems:

1. Local Trajectory Optimization: The core concept revolves around continuously optimizing the trajectory of an agent's actions to adapt efficiently to dynamic environments. By employing approaches like Model Predictive Control (MPC), the system can stabilize and accelerate value function learning despite approximation errors, allowing for near-optimal action computation.
2. Approximate Value Function Learning: The paper addresses the limitations often associated with approximate value function utilization by incorporating local trajectory optimization, which can reduce the reliance on long planning horizons and potentially improve policy performance. This dual approach mitigates the impact of errors inherent in value function approximators.
3. Planning for Exploration: Highlighting the shortfalls of traditional exploration strategies like $\epsilon$-greedy, POLO introduces temporally coordinated exploration. This is achieved by hypothesizing potentially rewarding areas in the state space and executing optimized exploratory trajectories using MPC. This method allows for efficient exploration conducive to more rapid learning.

Empirical Validation

Empirical evidence supporting the POLO framework is presented through experiments involving high-dimensional control tasks, such as humanoid locomotion and dexterous manipulation. The results showcase:

• Enhanced Exploration Efficacy: POLO demonstrates superior state space coverage in environments without explicit rewards when compared to traditional exploration strategies.
• Synergistic Use of Value Functions: The system's learned value functions can reduce the necessary planning horizons for effective control, indicating a robust capacity to generalize and retain task-specific knowledge.
• Stability in Learning: By employing trajectory optimization in conjunction with multi-step value updates, the framework achieved accelerated and stable learning outcomes.

Implications and Future Directions

The theoretical and empirical insights offered by this paper position POLO as a compelling advancement in the domain of model-based reinforcement learning. It effectively leverages computational models for both decision-making and exploratory behavior, thus facilitating rapid adaptation in the agent within complex environments.

Future directions for this research could explore the integration of model learning to complement the existing framework, thereby accommodating environments where internal models may be initially inaccurate. Another prospective avenue involves expanding the framework's application domains, particularly where rapid adaptation and learning are critical, beyond traditional robotics into emerging fields such as autonomous vehicles or adaptive game-playing AI systems.

This paper has significant practical implications, primarily in robotic systems, where it could redefine the efficiency and efficacy of planning and learning processes. By bridging the gap between localized control optimization and global policy learning, POLO offers a pliable framework that might improve decision-making systems under constrained real-world conditions.