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salesforce / ai-economist

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Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).

ai ml deep-reinforcement-learning economics multi-agent simulation-framework python-3 multi-agent-reinforcement-learning reinforcement-learning-environments
  • Updated Aug 8, 2020
  • Python

PathPlanning / AA-SIPP-m

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Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

pathfinding multi-agent sipp mapf pathplanning any-angle
  • Updated Jul 7, 2020
  • C++

BY571 / Deep-Reinforcement-Learning-Algorithm-Collection

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Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.

machine-learning library reinforcement-learning algorithms deep-reinforcement-learning multi-agent dqn policy-gradient reinforcement-learning-algorithms ddpg sac curiosity actor-critic intrinsic-motivation ppo a2c multi-agent-reinforcement-learning continuous-action-space soft-actor-critic intrinsic-curiosity-module
  • Updated Jul 13, 2020
  • Jupyter Notebook

xuedong / machine-learning-summer-schools

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Curated materials for different machine learning related summer schools

machine-learning natural-language-processing statistics reinforcement-learning deep-learning optimization automatic-differentiation deep-reinforcement-learning multi-agent generative-adversarial-network transfer-learning kernel-methods mcmc gaussian-processes variational-inference multi-armed-bandits probabilistic-graphical-models causal-inference optimal-transport interpretability
  • Updated Jul 8, 2020
  • Jupyter Notebook

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