We build training environments with tactical subject matter experts (SMEs), not guesswork. These simulations mimic the chaos of the real world, bad comms, jammed sensors, enemy tactics, multi-agent ops.

SMEs provide real operator runs to kickstart training and refine behavior. This keeps the AI grounded in reality, not theory.

We don’t treat explainability as a bolt-on. It’s baked in. Using inverse RL, we align AI decisions with what operators actually care about. The outputs plug cleanly into command-and-control workflows, whether you’re assisting, supervising, or going full-autonomy.

In reinforcement learning, an agent learns by interacting with an environment. It observes a state, takes an action, receives a reward, and updates its policy, the decision function, based on how effective that action was at achieving the desired outcome. Over time, this process drives the emergence of optimized behavior without requiring explicit programming.

We use RL in two principal ways:

• Proximal Policy Optimization (PPO) is used for continuous action spaces, such as adjusting velocity, heading, or camera orientation on UxS platforms. It’s stable and sample-efficient for tasks where smooth control is essential.

• Deep Q-Learning (DQN) is applied in discrete domains, such as selecting between pre-defined mission behaviors (search vs. pursue, loiter vs. return). It estimates the expected value of actions and selects those with the highest projected return.

Both methods work by maximizing expected cumulative reward over time, allowing policies to evolve across complex, delayed feedback structures, where it’s not obvious which immediate action leads to long-term success.

RL becomes especially valuable in:

• Partially observable environments (e.g., occluded targets, jammed sensors)

• Non-stationary dynamics (e.g., changing terrain, adaptive adversaries)

• Sparse feedback domains (e.g., outcomes only visible after long delays)

RL alone is often inefficient at early stages particularly in high-dimensional environments or where unsafe exploration isn’t viable. To accelerate learning, we use behavior cloning.

Behavior cloning is a supervised learning method that trains a policy to imitate expert behavior. SMEs perform representative mission runs, which are logged as sequences of states and actions. The AI model is then trained to replicate this mapping directly.

This yields a functional baseline policy that reflects SME decision-making. It’s often used as:

• An initial policy to bootstrap RL training

• A fallback behavior under uncertainty or degraded inputs

• A fixed imitation mode for tasks where exploration is too risky
  

Cloned policies are also benchmarked to ensure AI behaviors stay within SME-approved bounds.

When expert demonstrations are available, but no explicit reward function is defined, we apply inverse reinforcement learning (IRL). Instead of copying actions, IRL infers the why the objective behind the behavior.

The model observes expert trajectories and reconstructs a reward function that would have made those decisions optimal. Once recovered, this reward is used to train new policies that generalize to novel scenarios.

This is critical for:

• High-level behavior alignment, e.g., mission efficiency vs. stealth vs. survivability

• Dynamic objectives, where operator priorities shift over time

• Human-AI teaming, where shared intent must be maintained across changing conditions
  

IRL ensures the AI doesn’t just mimic, but internalizes operator goals in a way that adapts to new inputs.

Trained policies are optimized and deployed to run on embedded tactical systems. We target edge environments with:

• Quantized models for low-SWaP processors
• Hardware-accelerated inference for strict latency requirements
• Peer-to-peer coordination across unmanned assets, without centralized command
  

All learning components are integrated for offline deployment. We do not depend on cloud infrastructure, and learning pipelines are pre-compiled for field use. Local adaptation may be performed using episodic memory, delta updates from field logs, or policy switching logic when conditions deviate.

We don’t apply reinforcement learning for novelty, we apply it where conventional control logic breaks down. In environments with shifting dynamics, delayed outcomes, sparse feedback, or unpredictable adversaries, pre-defined control strategies fail to scale. Reinforcement learning overcomes these constraints by enabling policies to be learned, not programmed, optimized directly through interaction with the environment.

At Deca Defense, we use RL where the mission demands more than scripted behavior. Where hand-tuned logic can’t adapt, policy learning lets the system discover and refine strategies in ways no static architecture can. It’s not a replacement for traditional approaches, it’s what makes autonomy viable when traditional methods no longer apply.

This isn’t theoretical. It’s been tested, trained, and deployed under constraints where real-time decisions must match the complexity of the fight. Reinforcement learning is how Deca builds AI that can operate, adapt, and win, when the margin for error is zero.