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Found 8 papers, 6 papers with code
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
DeepMind Lab2D
We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design.
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games.
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016).
A multi-agent reinforcement learning model of common-pool resource appropriation
Here we show that deep reinforcement learning can be used instead.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
DeepMind Lab
DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems.
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinforcement learning.
Ranked #17 on
Atari Games
on Atari 2600 Private Eye
Human level control through deep reinforcement learning
We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.
