Presear builds production RL systems — from simulation environments to deployed policies — for robotics, logistics, trading, and autonomous control.
Technical Depth
From value-based methods to inverse RL — we apply the algorithm that fits your environment dynamics, reward signal, and deployment constraints.
Value-based RL for discrete action spaces — learning to approximate Q-values with deep neural networks and experience replay. We build DQN, Double DQN, Dueling DQN, and prioritised replay variants for sequential decision problems where the action set is finite and well-defined.
The workhorse of modern policy gradient methods — stable, sample-efficient, and effective across continuous and discrete action spaces. We use PPO as the default algorithm for robotics locomotion, game playing, and resource scheduling tasks due to its balance of performance and training reliability.
Combining a policy network (actor) with a value network (critic) for low-variance gradient estimates. Soft Actor-Critic (SAC) adds entropy maximisation for robust exploration, making it our preferred algorithm for continuous robotic control and locomotion tasks requiring smooth, generalising policies.
Training populations of interacting agents — cooperative, competitive, or mixed — for multi-robot coordination, market simulation, and network resource allocation. We build MARL systems using centralised training with decentralised execution (CTDE) and emergent communication protocols for complex coordination tasks.
Learning an explicit world model alongside the policy to enable planning with simulated rollouts — dramatically improving sample efficiency in data-scarce real-world environments. We apply Dyna-style and latent-space world models (Dreamer, MuZero) where simulation cost is high or real-world data collection is dangerous.
Learning a reward function from expert demonstrations rather than specifying it manually — solving the hardest part of real-world RL deployment. We use IRL and imitation learning (GAIL, AIRL, behaviour cloning) to bootstrap policies from human or expert data when reward engineering is infeasible or unsafe.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
The quality of an RL system is bounded by the fidelity and speed of its environment. We build custom simulation environments using physics engines (MuJoCo, Isaac Sim, PyBullet) or digital twins of existing systems — ensuring they faithfully represent the state space, action space, and dynamics the deployed policy will encounter.
The reward function is the most consequential design decision in any RL project. Poorly shaped rewards lead to reward hacking, unsafe behaviours, and policies that optimise the proxy metric while ignoring the true objective. We iteratively design, stress-test, and refine reward functions using human feedback and constraint-based shaping.
Cold-start exploration in complex environments is notoriously inefficient. We bootstrap policies using behaviour cloning from expert demonstrations, curriculum learning that progresses from simple to complex tasks, and pre-trained perception encoders — dramatically accelerating convergence and reducing simulation compute costs.
RL training at scale requires massively parallel rollouts. We use distributed RL frameworks — RLlib, Sample Factory — to run thousands of parallel environment instances across GPU or CPU clusters, accelerating convergence from weeks to hours while tracking all experiments for reproducibility and policy versioning.
The sim-to-real gap is where most RL projects fail in production. We bridge it through domain randomisation, adversarial training on varied simulation parameters, and staged real-world fine-tuning with safety constraints — delivering policies that maintain near-simulation performance under real-world noise, latency, and sensor imperfections.
Real-World Impact
Production reinforcement learning deployments across industries — agents that learn, adapt, and consistently outperform rule-based baselines.
Core Challenge
Industrial robotic arms programmed with fixed motion trajectories cannot adapt to part variability, positional uncertainty, or novel assembly configurations — requiring costly re-programming for every product change. RL-trained policies learn generalising grasping and manipulation behaviours that adapt on the fly.
Who Benefits
Automotive manufacturers, electronics assembly lines, pharmaceutical packaging facilities, and warehouse automation operators that need dexterous robotic manipulation capable of handling part variation without manual re-programming.
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Supply chain operations involve thousands of interdependent decisions — inventory replenishment, routing, load scheduling — where traditional OR solvers struggle with stochastic demand, real-time disruptions, and multi-echelon complexity at the scale and speed modern logistics requires.
Who Benefits
E-commerce logistics companies, 3PLs, FMCG distributors, and manufacturing supply chains that need AI-driven planning capable of adapting to real-time demand signals, disruptions, and multi-objective trade-offs between cost, speed, and service level.
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Financial markets are non-stationary environments where signal relationships shift continuously. Static rule-based strategies become obsolete quickly, and supervised models trained on historical data cannot adapt to regime changes in real time — leaving performance deterioration until manual retuning occurs.
Who Benefits
Proprietary trading desks, quantitative hedge funds, systematic strategy teams, and crypto trading operations that need adaptive execution and portfolio allocation agents capable of continuously learning from live market feedback.
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Game AI built with scripted behaviour trees requires months of manual tuning per title, produces predictable and exploitable opponents, and cannot adapt to evolving player strategies. RL agents trained through self-play develop emergent, human-level strategies without hand-crafted rules.
Who Benefits
Game studios building adaptive NPC AI, research teams using game environments as RL benchmarks, and simulation companies that need diverse, unpredictable agent populations for stress-testing autonomous systems and training data generation.
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Simulation engines, distributed RL frameworks, and experiment tracking tools — chosen for speed, scalability, and real-world deployment reliability.
Frequently Asked
Answers to the questions engineering leaders, CTOs, and operations teams ask before starting an RL engagement with Presear Softwares.
Ask Our RL TeamPartner with Presear Softwares to build RL systems that go beyond simulation — trained rigorously, transferred safely, and designed to adapt as your environment changes.