本文属于多智能体强化学习(MARL)领域,我们知道多智能体强化学习与传统的强化学习之间的区别在于,它有多个智能体(即agent),每一个agent要最大化各自的 reward function,他们之间可能是合作关系(当他们共享同一个reward function时)或者竞争关系(当他们零和博弈时)。另一个值得讨论的点在于 agent之间是否存在信息的交换...
KiloBot-MultiAgent-RL This is an experimentation to learn about Swarm Robotics with help of MultiAgent Reinforcement learning. We have used KiloBot as a platform as these are very simple in the actions space and have very high degree of symmetry. The Main inspiration of this project is this p...
whenever these external agents adapt and alter their behaviors, the underlying model distribution in the perspective of the agent also changes, rendering it to seem non-stationarity
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more c...
python3 main.py --base-dir [base_dir] evaluate --agents [agent] --evaluation-seeds [seed] --demo It is recommended to have only one agent and one evaluation seed for the demo run. This will launch the SUMO GUI, and./large_grid/data/view.xmlcan be applied to visualize queue length...
Agents are distributed evenly along the wall, with each agent obtaining state information wall-normal height hm away from the wall, computing the reward at the wall and supplying into the policy π to obtain actions a for the next time step. Full size image In order for the RL to be univ...
Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve thei...
Please contribute! Please take a look at ourcontributing guidefor how to add an environment/algorithm or submit a bug report. Our roadmap also lives there. Citing JaxMARL 📜 @article{flair2023jaxmarl, title={JaxMARL: Multi-Agent RL Environments in JAX}, author={Alexander Rutherford and Benj...
Besides, the multi-agent model demonstrates its advantage in balancing the risk and revenue, in comparison with the single-agent models. Additionally, The generalization experiments confirm that the proposed MADDQN method after pre-training in the proposed mixed dataset could be stably transferred to ...
The multi-agent system uses reinforcement learning algorithms to perform unsupervised learning. An excellent review of reinforcement learning agents can be seen in [18], [22], [27]. We give a brief introduction to reinforcement learning in the next section. ...