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 …
Pre-training contextualized world models with in-the-wild videos for reinforcement learning
Unsupervised pre-training methods utilizing large and diverse datasets have achieved
tremendous success across a range of domains. Recent work has investigated such …
tremendous success across a range of domains. Recent work has investigated such …
Redeeming intrinsic rewards via constrained optimization
State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (eg,
$\epsilon $-greedy) for exploration, but this method fails on hard exploration tasks like …
$\epsilon $-greedy) for exploration, but this method fails on hard exploration tasks like …
Accelerating reinforcement learning with value-conditional state entropy exploration
A promising technique for exploration is to maximize the entropy of visited state distribution,
ie, state entropy, by encouraging uniform coverage of visited state space. While it has been …
ie, state entropy, by encouraging uniform coverage of visited state space. While it has been …
Conditional mutual information for disentangled representations in reinforcement learning
Reinforcement Learning (RL) environments can produce training data with spurious
correlations between features due to the amount of training data or its limited feature …
correlations between features due to the amount of training data or its limited feature …
Having multiple selves helps learning agents explore and adapt in complex changing worlds
Satisfying a variety of conflicting needs in a changing environment is a fundamental
challenge for any adaptive agent. Here, we show that designing an agent in a modular …
challenge for any adaptive agent. Here, we show that designing an agent in a modular …
Intrinsic language-guided exploration for complex long-horizon robotic manipulation tasks
E Triantafyllidis, F Christianos… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Current reinforcement learning algorithms struggle in sparse and complex environments,
most notably in long-horizon manipulation tasks entailing a plethora of different sequences …
most notably in long-horizon manipulation tasks entailing a plethora of different sequences …
Deep reinforcement learning for multi-agent interaction
The development of autonomous agents which can interact with other agents to accomplish
a given task is a core area of research in artificial intelligence and machine learning …
a given task is a core area of research in artificial intelligence and machine learning …
Ensemble value functions for efficient exploration in multi-agent reinforcement learning
Cooperative multi-agent reinforcement learning (MARL) requires agents to explore to learn
to cooperate. Existing value-based MARL algorithms commonly rely on random exploration …
to cooperate. Existing value-based MARL algorithms commonly rely on random exploration …
Planning to go out-of-distribution in offline-to-online reinforcement learning
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or
OtO) is a paradigm well matched to a real-world RL deployment process. In this scenario, we …
OtO) is a paradigm well matched to a real-world RL deployment process. In this scenario, we …