Reinforcement Learning
Teaching AI to make smarter decisions through experience.
Reinforcement Learning (RL) enables AI systems to learn optimal behaviors through trial and feedback, dynamically adapting to complex and changing environments.
At Dot Square Lab, we develop RL solutions that go beyond static models, creating intelligent agents capable of self-improvement, strategic planning, and real-time decision-making.
We build scalable RL systems designed for practical deployment, not just research labs, ensuring real-world robustness and measurable business impact.
What we build with
Model-Free RL (Q-Learning, Policy Gradient Methods)
Model-Free RL (Q-Learning, Policy Gradient Methods)
Train agents without explicit environment models for flexibility and adaptability.
Model-Based RL
Model-Based RL
Use learned or provided environment models to plan actions more efficiently and accelerate learning.
Deep Reinforcement Learning
Deep Reinforcement Learning
Combine deep neural networks with reinforcement learning techniques to handle high-dimensional, complex state spaces.
Multi-Agent Systems
Multi-Agent Systems
Develop systems where multiple agents learn, collaborate, or compete, applicable in complex environments like logistics and autonomous fleets.
Reward Shaping and Curriculum Learning
Reward Shaping and Curriculum Learning
Design smarter training strategies to guide learning and improve convergence speed.
Offline and Batch RL
Offline and Batch RL
Train policies from existing datasets when real-time interaction is limited or risky.
Applications
Train policies from existing datasets when real-time interaction is limited or risky.
Train policies from existing datasets when real-time interaction is limited or risky.
Teach robots to navigate, manipulate objects, and adapt to real-world variability.
Dynamic Pricing and Bidding Systems
Dynamic Pricing and Bidding Systems
Optimize pricing strategies and bidding mechanisms in e-commerce, advertising, and marketplaces.
Resource Management and Optimization
Resource Management and Optimization
Maximize efficiency in energy grids, data centers, and manufacturing lines through intelligent resource allocation.
Personalization and Recommendations
Personalization and Recommendations
Continuously adapt recommendations and content personalization based on user interactions over time.
Finance and Portfolio Management
Finance and Portfolio Management
Develop adaptive strategies for portfolio optimization, trading, and risk management.
Game AI and Simulation Training
Game AI and Simulation Training
Create intelligent, adaptive agents for games, simulations, and training environments.
