Add to ORKGThis paper considers the problem of intelligent agent functioning in non-Markovian environments. We advice to divide the problem into two subproblems: just finding non-Markovian states in the environment and building an internal representation of original environment by the agent. The internal representation is free from non Markovian states because insufficient number of additional dynamically created states and transitions are provided. Then, the obtained environment might be used in classical reinforcement learning algorithms (like SARSA(λ)) which guarantee the convergence by Bellman equation. A great difficulty is to recognize different “copies” of the same states. The paper contains a theoretical introduction, ideas and problem descrip...
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Ma...
In applying reinforcement learning to agents acting in the real world we are often faced with tasks ...
To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical ext...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is t...
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is t...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Reinforcement learning in nonstationary environments is generally regarded as an important and yet d...
Reinforcement learning in non-stationary environments is generally regarded as a very difficult prob...
AbstractIt is widely acknowledged that biological beings (animals) are not Markov: modelers generall...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Ma...
In applying reinforcement learning to agents acting in the real world we are often faced with tasks ...
To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical ext...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is t...
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is t...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Reinforcement learning in nonstationary environments is generally regarded as an important and yet d...
Reinforcement learning in non-stationary environments is generally regarded as a very difficult prob...
AbstractIt is widely acknowledged that biological beings (animals) are not Markov: modelers generall...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Ma...
In applying reinforcement learning to agents acting in the real world we are often faced with tasks ...
To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical ext...