=GOAL:# choose an action based on epsilon-greedy algorithmifnp.random.binomial(1,EPSILON)==1:action=np.random.choice(ACTIONS)else:values_=q_value[state[0],state[1],:]action=np.random.choice([action_foraction_,v
Application performance and energy consumption deep exposed to task scheduling of nodes in wireless sensor networks (WSNs). Unreasonable task scheduling of nodes leads to excessive network energy consumption. Thus, a Q-learning algorithm for task scheduling based on Improved Support Vector Machine (IS...
PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method reinforcement-learningq-learningdqnreinforcement-learning-algorithmscontinuous-controlnafddpg-algorithmprioritized-experience-replaynormalized-advantage-functionsq-learning-algorithmn...
下面我们开始实现自己的Q-Learning import networkx as nximport numpy as npdef q_learning_shortest_path(G, start_node, end_node, learning_rate=0.8, discount_factor=0.95, epsilon=0.2, num_episodes=1000): """ Calculates the shortest path in a graph G using Q-learning algorithm. ...
""" Calculates the shortest path in a graph G using Q-learning algorithm. Parameters: G (networkx.Graph): the graph start_node: the starting node end_node: the destination node learning_rate (float): the learning rate (default=0.8) discount_factor (float): the discount factor (default=0.9...
课件地址: Advanced Q-learning algorithm.这节课继续讲解Q-learning algorithm,特别是DQN,并对常见的Q-learning algorithm给出了一个广义的视角描述,最后介绍了改善q-learning的一些技巧以及针对连续状态和动…
假设每个智能体能够知道ideal transition probability时即,当前动作下 ai 下,其余智能体采取最优联合动作: π−i∗(s,ai)=argmaxa−iQ(s,ai,a−i) ,导致在智能体i的角度环境动态为 Pi(s′|s,ai)=Penv(s′|s,ai,π−i∗(s,ai)) ,在这种情况下,假设最优策略只有一个,采用independent q-...
=GOAL:#choose an action based on epsilon-greedy algorithmifnp.random.binomial(1, EPSILON) == 1: action=np.random.choice(ACTIONS)else: values_= q_value[state[0], state[1], :] action= np.random.choice([action_foraction_, value_inenumerate(values_)ifvalue_ == np.max(values_)])...
# Q-learning algorithmforepisodeinrange(num_episodes):current_node=start_node_indexprint(episode)whilecurrent_node != end_node_index:# Choose action based on epsilon-greedy policyifnp.random.uniform(0,1) < epsilon:# Explorepossible_actions = np...
Calculates the shortest path in a graph G using Q-learning algorithm. Parameters: G (networkx.Graph): the graph start_node: the starting node end_node: the destination node learning_rate (float): the learning rate (default=0.8) discount_factor (float): the discount factor (default=0.95) ...