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 than sparse reproduction rewards. No reward shaping, no explicit cooperation signal.
Multi-Agent Deep Reinforcement Learning for Collaborative Computation Offloading in Mobile Edge-Computing
MADRL project solving chess environment using PPO with two different methods: 2 agents/networks and a single agent/network.
Training cooperative behaviour in Ghosts to capture Pac-Man using multi-agent deep reinforcement learning
Learned cooperation. Updated reproduction of sequential social dilemmas in RLlib new API stack (2.54.0)
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