Exploration in deep reinforcement learning: A survey
This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …
techniques are of primary importance when solving sparse reward problems. In sparse …
A review of deep reinforcement learning approaches for smart manufacturing in industry 4.0 and 5.0 framework
A del Real Torres, DS Andreiana, Á Ojeda Roldán… - Applied Sciences, 2022 - mdpi.com
In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Cooperative exploration for multi-agent deep reinforcement learning
Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …
attention. However, existing multi-agent deep reinforcement learning algorithms still use …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Winner takes it all: Training performant RL populations for combinatorial optimization
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …
Hierarchical reinforcement learning for air-to-air combat
Artificial Intelligence (AI) is becoming a critical component in the defense industry, as
recently demonstrated by DARPA's AlphaDogfight Trials (ADT). ADT sought to vet the …
recently demonstrated by DARPA's AlphaDogfight Trials (ADT). ADT sought to vet the …
Trajectory diversity for zero-shot coordination
We study the problem of zero-shot coordination (ZSC), where agents must independently
produce strategies for a collaborative game that are compatible with novel partners not seen …
produce strategies for a collaborative game that are compatible with novel partners not seen …
Effective diversity in population based reinforcement learning
Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …
data they acquire in the environment. With that in mind, maintaining a population of agents is …
Policy space diversity for non-transitive games
Abstract Policy-Space Response Oracles (PSRO) is an influential algorithm framework for
approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous …
approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous …