Search on the Replay Buffer: Bridging Planning and Reinforcement Learning (1906.05253v1)

Abstract: The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectively reason over long horizons, but assume access to a local policy and distance metric over collision-free paths. Reinforcement learning excels at learning policies and the relative values of states, but fails to plan over long horizons. Despite the successes of each method in various domains, tasks that require reasoning over long horizons with limited feedback and high-dimensional observations remain exceedingly challenging for both planning and reinforcement learning algorithms. Frustratingly, these sorts of tasks are potentially the most useful, as they are simple to design (a human only need to provide an example goal state) and avoid reward shaping, which can bias the agent towards finding a sub-optimal solution. We introduce a general control algorithm that combines the strengths of planning and reinforcement learning to effectively solve these tasks. Our aim is to decompose the task of reaching a distant goal state into a sequence of easier tasks, each of which corresponds to reaching a subgoal. Planning algorithms can automatically find these waypoints, but only if provided with suitable abstractions of the environment -- namely, a graph consisting of nodes and edges. Our main insight is that this graph can be constructed via reinforcement learning, where a goal-conditioned value function provides edge weights, and nodes are taken to be previously seen observations in a replay buffer. Using graph search over our replay buffer, we can automatically generate this sequence of subgoals, even in image-based environments. Our algorithm, search on the replay buffer (SoRB), enables agents to solve sparse reward tasks over one hundred steps, and generalizes substantially better than standard RL algorithms.

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

The research paper titled "Search on the Replay Buffer: Bridging Planning and Reinforcement Learning" presents an innovative approach known as "Search on the Replay Buffer" (SoRB) that combines the strengths of planning and reinforcement learning (RL) for solving complex, long-horizon tasks with sparse rewards. The work recognizes the complementary capabilities of planning algorithms—which excel at reasoning over extended horizons but struggle with high-dimensional observations—and RL, which effectively learns policies but often fails in long-term planning.

Core Proposition and Methodology

The central idea of this work is to leverage a replay buffer in RL to synthesize a practical search mechanism akin to planning. The method involves using a goal-conditioned value function to provide edge weights in a graph constructed from past observations stored within the replay buffer. Key components of the methodology include:

• Graph Construction: Nodes represent previously observed states, while edges are weighted by the predicted distance between states, measured using the RL-trained value function. The resultant graph facilitates waypoint discovery for reaching distant goals.
• Graph Search: The constructed graph permits the use of search algorithms to find sequences of subgoals, which the agent can then achieve iteratively, leveraging a goal-conditioned policy. This methodology is applicable even in high-dimensional, image-based environments.

Empirical Results

The empirical evaluation of SoRB demonstrates its effectiveness in overcoming challenges associated with sparse rewards and high dimensionality. Notably, the research highlights:

• Long-Horizon Task Performance: SoRB achieves substantial success in tasks requiring over 100 steps, significantly outperforming standard RL algorithms on both 2D and 3D navigation tasks.
• Generalization: The algorithm shows robustness and generalization across unseen environments, suggesting its applicability to a broad range of complex scenarios.
• Comparison with Baselines: In comparisons with state-of-the-art methods, including Semi-Parametric Topological Memory (SPTM), SoRB shows enhanced performance, particularly in distant goal-reaching tasks, underscored by superior success rates.

Implications and Future Directions

The introduction of SoRB offers meaningful contributions to both practical applications and theoretical frameworks in RL and planning. Practical implications include improved navigation capabilities in robotics and potential advancements in autonomous systems where long-term planning with sparse feedback is critical.

Theoretically, the paper introduces a promising direction for further integrating planning mechanisms within RL frameworks. Future developments could explore enhancements in goal-conditioned policy learning, more sophisticated planning algorithms intertwined with RL, and potential adaptations to broader domains such as automated control systems and interactive AI agents. Additionally, exploration into uncertainty quantification could refine distance estimations, increasing the robustness of the planning process within RL contexts.

In summary, the SoRB framework represents a significant stride in bridging planning with RL. The innovative use of replay buffers to enhance planning capabilities demonstrates potential transformative impacts on how agents can achieve complex, long-horizon goals, opening avenues for further exploration and refinement in artificial intelligence.