Add to ORKGThe options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the problem the agent is currently solving, they cannot be ported to other similar tasks that have different state spaces. We introduce the notion of learning options in agent-space, the portion of the agent’s sensation that is present and retains the same semantics across successive problem instances, rather than in problem-space. Agent-space options can be reused in later tasks that share the same agent-space but are sufficiently distinct to require different problem-spaces. We present experimental results that demonstrate the use of agent-space options in building ...
Transfer learning in reinforcement learning has been an active area of research over the past decade...
Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge d...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
The options framework provides methods for reinforcement learning agents to build new high-level ski...
Reinforcement Learning (RL) is a successful technique to train autonomous agents. However, the cla...
Reinforcement learning algorithms are very effective at learning policies (mappings from states to a...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Agents in reinforcement learning [2] tasks may learn slowly in large or complex tasks – transfer lea...
Options represent a formal way of adding tem-poral abstraction to reinforcement learning. They have ...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
We present an expressive agent design language for reinforcement learn-ing that allows the user to c...
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tai...
This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple ag...
© Springer International Publishing Switzerland 2015. The options framework provides a foundation to...
Transfer learning in reinforcement learning has been an active area of research over the past decade...
Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge d...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...
The options framework provides methods for reinforcement learning agents to build new high-level ski...
Reinforcement Learning (RL) is a successful technique to train autonomous agents. However, the cla...
Reinforcement learning algorithms are very effective at learning policies (mappings from states to a...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Agents in reinforcement learning [2] tasks may learn slowly in large or complex tasks – transfer lea...
Options represent a formal way of adding tem-poral abstraction to reinforcement learning. They have ...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
We present an expressive agent design language for reinforcement learn-ing that allows the user to c...
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tai...
This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple ag...
© Springer International Publishing Switzerland 2015. The options framework provides a foundation to...
Transfer learning in reinforcement learning has been an active area of research over the past decade...
Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge d...
Agents, physical and virtual entities that interact with theirenvironment, are becoming increasingly...