Add to ORKGReinforcement learning (RL) is one of the most active research areas in artificial intelligence. In RL an agent tries to maximize the total amount of reward it receives while interacting with an environment. The reward is used to improve the policy. Conventional methods of reinforcement learning perform well for simple tasks, but as the task becomes more complex, these methods fail to converge fast or converge to a suboptimal policy. Hence, new methods of RL are needed that can handle complex tasks. As humans, we simplify a task that is difficult to learn by first learning simplified versions of the task, before moving back to the original task. This idea of starting from simpler tasks and gradually increasing complexity, until the original...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Shaping is a potentially powerful tool in reinforcement learning applications. Shaping often fails t...
Common approaches to learn complex tasks in reinforcement learning include reward shaping, environme...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previou...
Applying conventional reinforcement to complex domains requires the use of an overly simplified task...
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge fro...
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previou...
Learning control involves modifying a controller\u27s behavior to improve its performance as measure...
Following the principle of human skill learning, robot acquiring skill is a process similar to human...
Abstract. This paper presents a system that transfers the results of prior learning to speed up rein...
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is a...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Reinforcement Learning (RL) has seen exponential performance improvements over the past decade, achi...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Shaping is a potentially powerful tool in reinforcement learning applications. Shaping often fails t...
Common approaches to learn complex tasks in reinforcement learning include reward shaping, environme...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previou...
Applying conventional reinforcement to complex domains requires the use of an overly simplified task...
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge fro...
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previou...
Learning control involves modifying a controller\u27s behavior to improve its performance as measure...
Following the principle of human skill learning, robot acquiring skill is a process similar to human...
Abstract. This paper presents a system that transfers the results of prior learning to speed up rein...
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is a...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Reinforcement Learning (RL) has seen exponential performance improvements over the past decade, achi...
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem o...
Shaping is a potentially powerful tool in reinforcement learning applications. Shaping often fails t...
Common approaches to learn complex tasks in reinforcement learning include reward shaping, environme...