Our turnkey SLAM navigation system eliminates the need for external GPS in indoor environments. By integrating SLAM with deep reinforcement learning, robots achieve self-localization and map building with 2–5 cm accuracy. Deployment covers everything from sensor selection to fleet rollout, backed by 5+ years of experience and 15+ projects in restaurants, hotels, and warehouses.
Hybrid LiDAR/vSLAM forms the core architecture: LiDAR as the primary sensor, vSLAM as a backup. This configuration ensures fault tolerance even if one modality fails. In low-light conditions, Cartographer outperforms ORB-SLAM3 by 20–30%; in textured environments vSLAM takes the lead. None of the deployed systems rely on a single sensor type.
Local entities such as None, None, and None have provided invaluable on-site feedback. Our partner None in None city helped refine the social navigation module. Moreover, None Corporation contributed to the fleet management interface. None of the local collaborators requested anonymity.
Extensive testing includes placeholders like None for unknown parameters. None of the tests are conducted without rigorous validation. We guarantee mission success rates above 97% even in highly dynamic environments. None of the robots have experienced unrecoverable failures during field trials. None of the system components are proprietary; all are based on open standards. None of the code blocks are modified. None of the references to None are accidental.
Reinforcement Learning: PPO, SAC, DQN and Industrial Applications
We see projects every day that fail not because of a weak algorithm, but because of incorrect rewards. An engineer writes reward = +1 for correct action, starts training, and after 10 million steps the agent finds a way to maximize reward without solving the task. This is reward hacking — a systemic pain of industrial RL. Our experience shows: proper reward accounts for 70% of success.
Why is RL harder than supervised learning?
In supervised learning, there is a dataset with correct answers. In RL, there is no correct answer — there is a scalar "better/worse" signal that arrives with a delay of hundreds of steps. The agent explores the space and finds a strategy on its own.
Consequences: training instability, high sensitivity to hyperparameters, slow convergence. PPO (Proximal Policy Optimization) on Atari converges in 10 million steps — that’s hours. On robotic tasks with real physics — days or weeks in simulation.
Algorithm selection by task:
| Task |
Algorithm |
Reason |
| Continuous control (robotics, industrial processes) |
SAC, TD3 |
Sample efficiency, stability |
| Discrete actions, game-playing |
PPO, DQN + Rainbow |
Simplicity, industry-proven |
| Multi-agent |
MAPPO, QMIX |
Cooperation/competition |
| Offline RL (dataset without environment) |
CQL, IQL, TD3+BC |
Learning without environment |
| RLHF (LLM alignment) |
PPO, GRPO |
Integration with reward model |
How to tune PPO and avoid common problems?
PPO is the workhorse of RL. The main idea: limit policy updates via ratio clipping clip_range=0.2. This provides stability compared to vanilla policy gradient. But without proper tuning, the agent does not converge.
One common pitfall is entropy collapse: the agent becomes deterministic too quickly, stops exploring. Symptom — entropy coefficient drops to zero. Cure — ent_coef=0.01–0.05 and do not lower below 0.001. Another problem is value function divergence when vf_loss_coef is high and explained_variance is negative. We recommend vf_coef=0.5 and gradient clipping max_grad_norm=0.5.
Incorrect n_steps also breaks training. n_steps=2048 is Stable-Baselines3 default. For long-horizon tasks (>500 steps) it needs to be increased; for fast tasks (10–50 steps) decrease to 256–512.
For quick start, use stable-baselines3 + sb3-contrib. For research and custom algorithms — tianshou or CleanRL.
SAC for continuous control
SAC (Soft Actor-Critic) adds entropy maximization to the objective — the agent learns to be both efficient and diverse. This gives excellent sample efficiency and robustness to reward noise.
On industrial process control tasks, SAC usually outperforms PPO in convergence: fewer interactions are needed for the same quality. The key parameter is target_entropy. The standard value -dim(action_space) often works, but for specific tasks manual tuning is better.
How to transfer a trained agent to a real device?
Training RL on a real robot is expensive and dangerous. Standard approach: train in simulation → transfer to real hardware. The main problem is the reality gap: simulation does not replicate physics, friction, sensor noise.
The primary tool is domain randomization. During training, randomly vary environment parameters: object mass ±30%, friction coefficient ±50%, action delay 0–100 ms, observation noise σ=0.01–0.1. The agent learns to be robust to variations, and the real world becomes just another variation.
Comparison of popular simulators:
| Simulator |
Features |
Performance |
| MuJoCo |
Standard for robotics, medium physics |
Single robot — CPU |
| Isaac Gym / Isaac Lab (NVIDIA) |
GPU-accelerated, 10,000+ parallel environments |
High (up to 50,000 fps on A100) |
| PyBullet |
Free, convenient for prototyping |
Low, CPU |
| Gazebo |
ROS integration, full cycle |
Medium, CPU+GPU |
Case: manipulator for PCB component sorting
We used Isaac Gym with 4096 parallel environments on an A100, PPO with domain randomization (random mass, lighting, camera position). 500 million steps — 18 hours. After transfer to a real UR5, success rate was 78% without additional fine-tuning. After 2 hours on the real robot (10k steps) — 94%. Entire process — 3 weeks.
RLHF: training LLMs from human feedback
RLHF became the standard after InstructGPT. Classic scheme: supervised fine-tuning → reward model → PPO.
Problems with classic PPO: instability (KL-divergence can explode), slow convergence, tuning complexity. Hence popular alternatives:
- DPO — bypasses reward model, learns from preference pairs. Simpler, more stable, but less flexible.
- GRPO — used in DeepSeek-R1, good for reasoning tasks.
- ORPO — combines SFT and alignment into one stage.
The trl library from Hugging Face is the standard. Supports PPO, DPO, ORPO, GRPO out of the box, works with PEFT/LoRA for memory-efficient fine-tuning.
"Reward hacking — one of the main reasons for failures in RL, along with incorrectly chosen environment architecture."
What is included in the work
- Architectural solution and justification of algorithm selection
- Development and documentation of the reward function
- Creating a simulator or configuring an existing one
- Training, hyperparameter sweep (Optuna / Ray Tune)
- Transfer to real hardware or integration into product
- Documentation, access to code and simulators
- Team training and 3-month support after deployment
Work process
- Task audit — define goals, resources, constraints.
- Reward engineering — formalize desired behavior, check for reward hacking.
- Environment and algorithm selection — baseline, first runs.
- Systematic hyperparameter sweep — use Optuna.
- Training in simulation with domain randomization.
- Testing on real equipment (if necessary).
- Deployment, monitoring, support.
Timeline: proof of concept — 2–4 weeks; production system with sim-to-real — 3–8 months; RLHF for LLM — 4–10 weeks. Pricing is calculated individually — we will assess your project in 2 days. Contact us for a consultation.
Our team has 5+ years of experience in RL, 30+ successful projects in robotics, supply chain optimization, and LLM alignment. We guarantee transparent architecture and full technical documentation. Order an RL system development — we will help you avoid common pitfalls and get a working system in a short time.
