Add to ORKGA longstanding goal of reinforcement learning is to develop nonparametric representations of policies and value functions that support rapid learning without suffering from interference or the curse of dimensionality. We have developed a trajectory-based approach, in which policies and value functions are represented nonparametrically along trajectories. These trajectories, policies, and value functions are updated as the value function becomes more accurate or as a model of the task is updated. We have applied this approach to periodic tasks such as hopping and walking, which required handling discount factors and discontinuities in the task dynamics, and using function approximation to represent value functions at discontinuities. We also...
Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks gi...
We present a general, two-stage reinforcement learning approach to create robust policies that can b...
The dilemma between exploration and exploitation is an important topic in reinforcement learning (RL...
A longstanding goal of reinforcement learning is to develop nonparametric representations of policie...
To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuou...
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Mar...
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially wi...
Abstract. Approximate value iteration methods for reinforcement learn-ing (RL) generalize experience...
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Mar...
The standard feedback model of reinforcement learning requires revealing the reward of every visited...
International audienceReinforcement learning is a machine learning answer to the optimal control pro...
This paper considers trajectory tracking control for a nonholonomic mobile robot using integral rein...
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can lear...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
The application of reinforcement learning to problems with continuous domains requires representing ...
Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks gi...
We present a general, two-stage reinforcement learning approach to create robust policies that can b...
The dilemma between exploration and exploitation is an important topic in reinforcement learning (RL...
A longstanding goal of reinforcement learning is to develop nonparametric representations of policie...
To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuou...
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Mar...
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially wi...
Abstract. Approximate value iteration methods for reinforcement learn-ing (RL) generalize experience...
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Mar...
The standard feedback model of reinforcement learning requires revealing the reward of every visited...
International audienceReinforcement learning is a machine learning answer to the optimal control pro...
This paper considers trajectory tracking control for a nonholonomic mobile robot using integral rein...
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can lear...
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, a...
The application of reinforcement learning to problems with continuous domains requires representing ...
Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks gi...
We present a general, two-stage reinforcement learning approach to create robust policies that can b...
The dilemma between exploration and exploitation is an important topic in reinforcement learning (RL...