A review on deep reinforcement learning for fluid mechanics
Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics
and engineering domains for its ability to solve decision-making problems that were …
and engineering domains for its ability to solve decision-making problems that were …
Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Stable-baselines3: Reliable reinforcement learning implementations
STABLE-BASELINES3 provides open-source implementations of deep reinforcement
learning (RL) algorithms in Python. The implementations have been benchmarked against …
learning (RL) algorithms in Python. The implementations have been benchmarked against …
The surprising effectiveness of ppo in cooperative multi-agent games
Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …
Pettingzoo: Gym for multi-agent reinforcement learning
This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Towards the systematic reporting of the energy and carbon footprints of machine learning
Accurate reporting of energy and carbon usage is essential for understanding the potential
climate impacts of machine learning research. We introduce a framework that makes this …
climate impacts of machine learning research. We introduce a framework that makes this …
Avalanche: an end-to-end library for continual learning
Learning continually from non-stationary data streams is a long-standing goal and a
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph
neural network (GNN) and reinforcement learning (RL). We formulate the scheduling …
neural network (GNN) and reinforcement learning (RL). We formulate the scheduling …
Monotonic value function factorisation for deep multi-agent reinforcement learning
In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …
decentralised fashion. At the same time, it is often possible to train the agents in a …
Deep reinforcement learning for automated stock trading: An ensemble strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …
a profitable strategy in a complex and dynamic stock market. In this paper, we propose an …