PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
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Updated
Nov 11, 2017 - Python
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Exploring learned cooperation, coevolution and free-riding. Learning is achieved through Multi-Agent Deep Reinforcement Learning (MADRL) in an ecological environment. The environment emits no other Reinforcement Learning rewards other than sparse reproduction rewards. No reward shaping, no explicit cooperation signal.
MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.
Multi-Agent Deep Reinforcement Learning for Collaborative Computation Offloading in Mobile Edge-Computing
Training cooperative behaviour in Ghosts to capture Pac-Man using multi-agent deep reinforcement learning
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