Philipp Altmann

Philipp Altmann

DRIVE

Implementations accompanying research on dynamic reward incentives for variable exchange (DRIVE), a decentralized peer-incentivization mechanism for emergent cooperation under changing rewards.

REACT

Implementations and video demonstrations acompanying research on revealing evolutionary action consequence trajectories (REACT) for interpretable reinforcement learning.

Quantum Circuit Designer

A gymnasium-based set of environments for benchmarking reinforcement learning for state preparation and unitary composition in quantum circuit design.

Niryo Gymnasium

A reinforcement learning MuJoCo gymnasium environment for benchmarking robotic grasping using the Niryo-NED2 robot arm.

Emergence in Multi-Agent Systems

Implementations accompanying research on a safety persepective on emergence in multi-agent systems and mitigation techniques.

DIRECT

Implementations accompanying research on discriminative reward co-training (DIRECT), a self-imitation architecture for robust reinforcement learning from sparse rewards.

hyphi-gym

A Gymnasium benchmark suite for evaluating the robustness and multi-task performance of reinforcement learning algorithms in various discrete and continuous environments.

CROP

Implementations accompanying research on distributional-shift robust reinforcement learning using compact reshaped observation processing (CROP).

Karting Challenge

Unity project for a karting-based ML‑Agents challenge. Drive a kart around a modular track, collect rewards, and train agents to complete laps robustly across different track parts and obstacles.

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