Self-Contained RL Agent for Planning, Memory & Tool Use

The article presents a step‑by‑step guide for building an agent that learns to plan, remember, and manipulate tools all within a single neural network, rather than relying on an external pipeline. The core innovation is a *stage‑aware actor‑critic* architecture that can switch between different sub‑tasks—such as arithmetic operations, memory retrieval, and tool selection—based on the current state of the environment. By training this network on a carefully designed curriculum that gradually increases in complexity, the agent learns to decompose a problem into a sequence of low‑level actions that together accomplish a higher‑level goal.

During training, the agent is exposed to a set of arithmetic reasoning tasks that require multiple tools (e.g., addition, subtraction, memory look‑ups). The curriculum starts with simple single‑step operations and progresses to multi‑step problems that demand strategic planning and the use of memory buffers. Because the agent’s policy network receives a *stage* embedding that signals which sub‑task is active, it can internally shift its focus and allocate resources accordingly. The reward structure is designed to encourage not only correct final answers but also efficient planning—penalizing unnecessary steps and encouraging the reuse of previously learned sub‑skills. As a result, the agent develops an internal memory that stores intermediate results, and a *tool‑selection* module that decides which arithmetic operation to apply at each step.

The final model is a lean, end‑to‑end system that solves complex reasoning problems without any external orchestrator or symbolic planner. By embedding planning, memory, and tool use directly into the neural weights, the agent achieves a level of flexibility and generality that is difficult to match with traditional pipeline approaches. Moreover, the authors demonstrate that the same framework can be extended to other domains—such as navigation or dialogue—by simply redefining the set of tools and the curriculum. This work showcases the power of reinforcement learning to bootstrap sophisticated cognitive capabilities within a unified architecture, paving the way for more autonomous and adaptable AI systems.