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Hierarchical reinforcement learning: A comprehensive survey
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
challenging long-horizon decision-making tasks into simpler subtasks. During the past …
An information-theoretic perspective on intrinsic motivation in reinforcement learning: A survey
The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …
new contributions, especially considering the emergent field of deep RL (DRL). However, a …
Offline reinforcement learning as one big sequence modeling problem
Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
Deep hierarchical planning from pixels
Intelligent agents need to select long sequences of actions to solve complex tasks. While
humans easily break down tasks into subgoals and reach them through millions of muscle …
humans easily break down tasks into subgoals and reach them through millions of muscle …
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Trading off exploration and exploitation in an unknown environment is key to maximising
expected return during learning. A Bayes-optimal policy, which does so optimally, conditions …
expected return during learning. A Bayes-optimal policy, which does so optimally, conditions …
Efficient exploration via state marginal matching
Exploration is critical to a reinforcement learning agent's performance in its given
environment. Prior exploration methods are often based on using heuristic auxiliary …
environment. Prior exploration methods are often based on using heuristic auxiliary …
Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …
sequence of motor actions. While deep reinforcement learning methods have recently …
Controllability-aware unsupervised skill discovery
One of the key capabilities of intelligent agents is the ability to discover useful skills without
external supervision. However, the current unsupervised skill discovery methods are often …
external supervision. However, the current unsupervised skill discovery methods are often …
Metra: Scalable unsupervised rl with metric-aware abstraction
Unsupervised pre-training strategies have proven to be highly effective in natural language
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …
Robot motion planning in learned latent spaces
This letter presents latent sampling-based motion planning (L-SBMP), a methodology
toward computing motion plans for complex robotic systems by learning a plannable latent …
toward computing motion plans for complex robotic systems by learning a plannable latent …