Reinforcement learning (RL) concerns the problem of a learning agent interacting with its environment to achieve a goal. Instead of being given examples of desired behavior, the learning agent must discover by trial and error how to behave in order to get the most reward. The environment is a Markov decision process (MDP) with state set, S, and action set, A. The agent and the environment inter...

An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs) and demanders (represented by DisCOs) compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricit...

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to app...

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces ...

Describes a novel neural architecture for learning deterministic context-free grammars, or equivalently, deterministic pushdown automata. The unique feature of the proposed network is that it forms stable state representations during learning-previous work has shown that conventional analog recurrent networks can be inherently unstable in that they cannot retain their state memory for long inpu...

Reinforcement learning methods, surviving the control difficulties of the unknown environment, are gaining more and more popularity recently in the autonomous robotics community. One of the possible difficulties of the reinforcement learning applications in complex situations is the huge size of the statevalueor action-value-function representation [2]. The case of continuous environment (conti...

As an important method to solve sequential decisionmaking problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to largescale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitat...

the main issues in any society are teaching and learning and main elements of this story are the teacher and the learner. there are different psychology schools in which any of them in turn, have taken many extensive researches about behavior, and facts and theories of learning have been studied from a particular perspective. rationalism asserts that the human intellect has the highest energy a...

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to app...

This work describes a neural network model which simulates a discrete part of the dopaminergic striatal circuitry involved in reinforcement learning. We consider the proposal that learning by reinforcement is acquired by a heterosynaptic mechanism a!ecting plasticity of corticostriatal synapses, under the modulatory control of DA neurons. The simulation results seem to be successful since they ...