32,200 research outputs found
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Exploration in multi-agent reinforcement learning is a challenging problem,
especially in environments with sparse rewards. We propose a general method for
efficient exploration by sharing experience amongst agents. Our proposed
algorithm, called Shared Experience Actor-Critic (SEAC), applies experience
sharing in an actor-critic framework. We evaluate SEAC in a collection of
sparse-reward multi-agent environments and find that it consistently
outperforms two baselines and two state-of-the-art algorithms by learning in
fewer steps and converging to higher returns. In some harder environments,
experience sharing makes the difference between learning to solve the task and
not learning at all.Comment: 34th Conference on Neural Information Processing Systems (NeurIPS
2020), Vancouver, Canad
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning. Deep Neural
Networks have proven to cope perfectly with all learning paradigms, i.e.
supervised, unsupervised, and reinforcement learning. Nevertheless, traditional
deep learning approaches make use of cloud computing facilities and do not
scale well to autonomous agents with low computational resources. Even in the
cloud, they suffer from computational and memory limitations, and they cannot
be used to model adequately large physical worlds for agents which assume
networks with billions of neurons. These issues are addressed in the last few
years by the emerging topic of sparse training, which trains sparse networks
from scratch. This paper discusses sparse training state-of-the-art, its
challenges and limitations while introducing a couple of new theoretical
research directions which has the potential of alleviating sparse training
limitations to push deep learning scalability well beyond its current
boundaries. Nevertheless, the theoretical advancements impact in complex
multi-agents settings is discussed from a real-world perspective, using the
smart grid case study
Multi-agent Deep Covering Option Discovery
The use of options can greatly accelerate exploration in reinforcement
learning, especially when only sparse reward signals are available. While
option discovery methods have been proposed for individual agents, in
multi-agent reinforcement learning settings, discovering collaborative options
that can coordinate the behavior of multiple agents and encourage them to visit
the under-explored regions of their joint state space has not been considered.
In this case, we propose Multi-agent Deep Covering Option Discovery, which
constructs the multi-agent options through minimizing the expected cover time
of the multiple agents' joint state space. Also, we propose a novel framework
to adopt the multi-agent options in the MARL process. In practice, a
multi-agent task can usually be divided into some sub-tasks, each of which can
be completed by a sub-group of the agents. Therefore, our algorithm framework
first leverages an attention mechanism to find collaborative agent sub-groups
that would benefit most from coordinated actions. Then, a hierarchical
algorithm, namely HA-MSAC, is developed to learn the multi-agent options for
each sub-group to complete their sub-tasks first, and then to integrate them
through a high-level policy as the solution of the whole task. This
hierarchical option construction allows our framework to strike a balance
between scalability and effective collaboration among the agents. The
evaluation based on multi-agent collaborative tasks shows that the proposed
algorithm can effectively capture the agent interactions with the attention
mechanism, successfully identify multi-agent options, and significantly
outperforms prior works using single-agent options or no options, in terms of
both faster exploration and higher task rewards.Comment: This paper was presented in part at the ICML Reinforcement Learning
for Real Life Workshop, July 202
- …