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The combination of mixed-motive scenarios, generalisation testing, and large populations makes Melting Pot a uniquely challenging and relevant multi-agent reinforcement learning benchmark. By hosting this contest at NeurIPS 2023, we aim to drive progress in cooperative AI and multi-agent learning, build consensus on metrics for cooperation, and ...
Melting Pot is available on PyPI and can be installed using: pip install dm-meltingpot. NOTE: Melting Pot is built on top of DeepMind Lab2D which is distributed as pre-built wheels. If there is no appropriate wheel for dmlab2d, you will need to build it from source (see the dmlab2d README.md for details).
We are excited to announce the Melting Pot Contest at NeurIPS 2023, organised by researchers from the Cooperative AI Foundation, MIT, and Google DeepMind. This new competition challenges researchers to push the boundaries of multi-agent reinforcement learning (MARL) for mixed-motive cooperation.
Gilson Melting Pots are ideal for preparing capping compounds, wax, tars, liquid asphalt, and other materials in the lab. Each Melting Pot has precision temperature controls that hold consistently to a set point from 100°–320°F (38°–160°C). A high limit control automatically turns pots off at 360°F (182°C).
Melting Pots are ideal for preparing capping compounds, wax, tars, liquid asphalt, and other materials in the lab. Each Melting Pot has precision temperature controls that hold consistently to a set point from 100°—320°F (38°—160°C). A high limit control automatically turns pots off at 360°F (182°C).
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Used to melt wax and other materials, it maintains heat from room temperature to max. 350 °C. Suitable also for general laboratory purposes. Complete with adjustable thermostat range +50 °C to +350 °C, accuracy ± 1.5 °C, pilot lamp fully isolated to CE requirements. Capacity: 5 litres.
