Temporal difference learning for model predictive control
Data-driven model predictive control has two key advantages over model-free methods: a
potential for improved sample efficiency through model learning, and better performance as …
potential for improved sample efficiency through model learning, and better performance as …
Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm
AK Jayant, S Bhatnagar - Advances in Neural Information …, 2022 - proceedings.neurips.cc
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents
perform a significant number of random exploratory steps. In the real world, this can limit the …
perform a significant number of random exploratory steps. In the real world, this can limit the …
Omnisafe: An infrastructure for accelerating safe reinforcement learning research
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense
potential to catalyze societal advancement, yet their deployment is often impeded by …
potential to catalyze societal advancement, yet their deployment is often impeded by …
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
Mastering the unsupervised reinforcement learning benchmark from pixels
Controlling artificial agents from visual sensory data is an arduous task. Reinforcement
learning (RL) algorithms can succeed but require large amounts of interactions between the …
learning (RL) algorithms can succeed but require large amounts of interactions between the …
Safe dreamerv3: Safe reinforcement learning with world models
The widespread application of Reinforcement Learning (RL) in real-world situations is yet to
come to fruition, largely as a result of its failure to satisfy the essential safety demands of …
come to fruition, largely as a result of its failure to satisfy the essential safety demands of …
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
Dual rl: Unification and new methods for reinforcement and imitation learning
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected
cumulative return. It has been shown that this objective can be represented as an …
cumulative return. It has been shown that this objective can be represented as an …
Reset-free lifelong learning with skill-space planning
The objective of lifelong reinforcement learning (RL) is to optimize agents which can
continuously adapt and interact in changing environments. However, current RL approaches …
continuously adapt and interact in changing environments. However, current RL approaches …
Predictable mdp abstraction for unsupervised model-based rl
A key component of model-based reinforcement learning (RL) is a dynamics model that
predicts the outcomes of actions. Errors in this predictive model can degrade the …
predicts the outcomes of actions. Errors in this predictive model can degrade the …