GeoVerse Lab
← AI & Computing Sciences Division

Reinforcement Learning & Agents Center

Develops reinforcement learning theory, autonomous agent architectures, and sequential decision-making systems - spanning deep RL, model-based world models, multi-agent systems, and LLM-based agents - for scientific exploration, experiment design, and adaptive control.

Machine Learning CenterNatural Language Processing CenterComputer Vision CenterHigh-Performance Computing CenterQuantum Computing CenterGenerative AI & Foundation Models CenterReinforcement Learning & Agents CenterAI Safety & Alignment CenterAI for Science CenterRobotics & Embodied AI Center
Research Fields 15
Markov Decision Processes & Optimal Stochastic Control
🗺️ Markov Decision Processes & Optimal Stochastic Control
A Markov decision process is the standard mathematical model of sequential decision-making…
Temporal-Difference & Associative Learning
🔔 Temporal-Difference & Associative Learning
Temporal-difference (TD) learning updates predictions from the difference between successi…
Policy Gradient & Direct Policy Optimization
⛰️ Policy Gradient & Direct Policy Optimization
Policy gradient methods parameterise the agent's policy directly, as a probability distrib…
Actor-Critic Methods & Hedonistic Neuron Theory
🎭 Actor-Critic Methods & Hedonistic Neuron Theory
Actor-critic architectures split a reinforcement-learning agent into two cooperating modul…
Model-Based RL & World Models
🔮 Model-Based RL & World Models
Model-based reinforcement learning equips an agent with an internal predictive model of it…
Multi-Agent Systems & Stochastic Games
🕸️ Multi-Agent Systems & Stochastic Games
Multi-agent systems study decision-making when several learners share one environment, eac…
Monte Carlo Tree Search & Self-Play Game Learning
♟️ Monte Carlo Tree Search & Self-Play Game Learning
Monte Carlo tree search (MCTS) decides moves by growing, one simulation at a time, a searc…
LLM-Based Autonomous Agents & Goal-Directed Behavior Theory
🤖 LLM-Based Autonomous Agents & Goal-Directed Behavior Theory
LLM-based agents are software systems in which a large language model supplies the reasoni…
Bayesian & Optimal Experimental Design for Science
🧪 Bayesian & Optimal Experimental Design for Science
Optimal experimental design chooses experimental conditions, what to measure, where, and w…
Exploration-Exploitation & Multi-Armed Bandit Theory
🎰 Exploration-Exploitation & Multi-Armed Bandit Theory
The exploration-exploitation dilemma is the fundamental tension of sequential decision-mak…
Hierarchical Reinforcement Learning & Goal Decomposition
🪜 Hierarchical Reinforcement Learning & Goal Decomposition
Hierarchical reinforcement learning introduces temporally extended actions into the MDP fr…
Imitation Learning & Observational/Social Learning
🪞 Imitation Learning & Observational/Social Learning
Imitation learning acquires behaviour from demonstrations by a competent agent rather than…
Intrinsic Motivation & Curiosity-Driven Exploration
✨ Intrinsic Motivation & Curiosity-Driven Exploration
Intrinsic motivation research builds reward signals that come from inside the learning age…
Sequential Games & Extensive-Form Analysis
♟︎ Sequential Games & Extensive-Form Analysis
Extensive-form game theory represents strategic interaction as a game tree: players move i…
Adaptive & Self-Tuning Control Systems
🎛️ Adaptive & Self-Tuning Control Systems
Adaptive control is the branch of control theory in which the controller adjusts its own p…