Habits, action sequences and reinforcement learning

Habits, action sequences and reinforcement learning.

It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquired and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and reward...

Extracting Action Sequences from Texts Based on Deep Reinforcement Learning

Extracting action sequences from texts in natural language is challenging, which requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require either the set of candidate actions is provided in advance, or action descriptions are restricted in a specific form, e.g., description templates....

Multi-Agent Reinforcement Learning with Bidding for Segmenting Action Sequences

Reinforcement Learning with Action Discovery

The design of reinforcement learning solutions to many problems artificially constrain the action set available to an agent, in order to limit the exploration/sample complexity. While exploring, if an agent can discover new actions that can break through the constraints of its basic/atomic action set, then the quality of the learned decision policy could improve. On the flipside, considering al...

Action discovery for reinforcement learning