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Stay Updated with the Latest in the Hong Kong Senior Shield

Football enthusiasts across Hong Kong, prepare to dive deep into the thrilling world of the Hong Kong Senior Shield! With fresh matches being updated daily, this is your go-to source for expert betting predictions and insights. Whether you're a seasoned bettor or new to the scene, our comprehensive coverage ensures you're always in the know.

Hong Kong

Senior Shield

What is the Hong Kong Senior Shield?

The Hong Kong Senior Shield, known locally as "Kasi ya Mabingwa wa Hong Kong," is one of the most prestigious football competitions in the region. Established to showcase local talent and foster competitive spirit, it brings together some of the best teams from across Hong Kong. The tournament is not just a display of skill but a celebration of football culture in the city.

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With matches being played frequently, staying updated can be a challenge. Our platform ensures that you receive real-time updates on every match. From goals scored to red cards shown, our detailed reports keep you in the loop, so you never miss a moment of action.

Expert Betting Predictions: Make Informed Bets

Betting on football can be both exciting and profitable if done right. Our team of expert analysts provides daily betting predictions based on thorough research and analysis. From team form to player injuries, we cover all angles to help you make informed betting decisions.

Match Previews: What to Expect

  • Team Form: Analyze how each team has been performing in recent matches.
  • Head-to-Head Stats: Understand past encounters between teams to gauge potential outcomes.
  • Key Players: Identify players who could turn the tide in favor of their teams.
  • Injury Reports: Stay informed about any player injuries that might impact team performance.

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Experience the thrill of live matches with our real-time coverage. Follow live scores, watch highlights, and get instant updates as the action unfolds. Our platform ensures you're right there in the stadium, even if you're miles away.

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  • Bet Responsibly: Always set limits and bet within your means.

In-Depth Analysis: Beyond the Basics

For those who crave more than just surface-level insights, our in-depth analysis section offers detailed breakdowns of tactics, formations, and strategies employed by teams. Understand the nuances that could influence match outcomes and enhance your betting strategy.

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We are dedicated to providing comprehensive coverage of the Hong Kong Senior Shield. With a team of passionate experts and a commitment to delivering quality content, we strive to be your trusted source for all things football-related in Hong Kong.

<|repo_name|>aravindgadde/ReinforcementLearning<|file_sep|>/multi_agent/multi_agent.py import random from collections import namedtuple import numpy as np import torch import torch.nn.functional as F import torch.optim as optim from model import ActorCritic from replay_memory import ReplayMemory device = torch.device("cuda" if torch.cuda.is_available() else "cpu") Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward')) class Agent(): def __init__(self): self.actor_critic = ActorCritic() self.memory = ReplayMemory(10000) self.target_actor_critic = ActorCritic() self.optimizer = optim.Adam(self.actor_critic.parameters(), lr=0.0001) self.last_state = None # hyperparameters self.gamma = .99 self.batch_size = BATCH_SIZE self.update_target_steps = UPDATE_TARGET_STEPS # for logging self.episode_durations = [] self.num_episodes = NUM_EPISODES def select_action(self): # state is N x state_dim tensor state_v = torch.FloatTensor(self.last_state).to(device) # get action probability distribution over actions from policy network (state) probs_v = self.actor_critic(state_v) * torch.FloatTensor([0.25] * ACTION_DIM).to(device) m = torch.distributions.Categorical(probs_v) action = m.sample().item() + random.randint(0,ACTION_DIM-4) # add offset so that only actions {0..ACTION_DIM-1} are possible return action def step(self): if len(self.memory) > self.batch_size: transitions = self.memory.sample(self.batch_size) batch = Transition(*zip(*transitions)) # compute discounted rewards backwards rewards = np.array(batch.reward) discounted_rewards = [] r = REWARD_NORM * rewards[-1] discounted_rewards.append(r) for reward in reversed(rewards[:-1]): r = reward + r * self.gamma discounted_rewards.append(r) discounted_rewards.reverse() # normalize discounted rewards (only works well when mean=0) mean_r = np.mean(discounted_rewards) std_r = np.std(discounted_rewards) discounted_rewards -= mean_r if std_r !=0: discounted_rewards /= std_r # convert everything into a PyTorch Variable rewards_v = torch.FloatTensor(discounted_rewards).to(device) state_batch_v = torch.FloatTensor(batch.state).to(device) next_state_batch_v = torch.FloatTensor(batch.next_state).to(device) action_batch_v = torch.LongTensor(batch.action).to(device) state_action_values_v = self.actor_critic(state_batch_v).gather(1, action_batch_v.unsqueeze(-1)).squeeze(-1) next_state_values_v = self.target_actor_critic(next_state_batch_v).max(1)[0].detach() expected_state_action_values_v = next_state_values_v * self.gamma + rewards_v loss_t = F.mse_loss(state_action_values_v, expected_state_action_values_v) - (self.actor_critic(state_batch_v)*torch.FloatTensor([0.25] * ACTION_DIM).to(device)).gather(1, action_batch_v.unsqueeze(-1)).mean() self.optimizer.zero_grad() loss_t.backward() nn.utils.clip_grad_norm_(self.actor_critic.parameters(), CLIP_GRAD) self.optimizer.step() def reset(self): # reset environment at end of episode if len(self.memory) >= BATCH_SIZE: self.target_actor_critic.load_state_dict(self.actor_critic.state_dict()) if __name__ == "__main__": env.reset() <|file_sep|># Reinforcement Learning This repository contains various implementations of RL algorithms. ### Multi-agent reinforcement learning (MARL) This is an implementation of multi-agent reinforcement learning using deep Q-learning algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every episode. The agents are trained using experience replay. ### Continuous control with deep reinforcement learning (DDPG) This is an implementation of continuous control using DDPG algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every step. The agents are trained using experience replay. ### Soft Actor-Critic (SAC) This is an implementation of SAC algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every step. The agents are trained using experience replay. ### Soft Q-learning (SQL) This is an implementation of SQL algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every step. The agents are trained using experience replay. ### Asynchronous Advantage Actor-Critic (A2C) This is an implementation of A2C algorithm. The agents are trained using an on-policy approach. Each agent has its own actor-critic model which are updated after every episode. ### Deep Deterministic Policy Gradient (DDPG) This is an implementation of DDPG algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every step. The agents are trained using experience replay. ### Deep Q-Network (DQN) This is an implementation of DQN algorithm. The agents are trained using an off-policy approach. Each agent has its own actor-critic model which are updated after every episode. The agents are trained using experience replay.<|repo_name|>aravindgadde/ReinforcementLearning<|file_sep|>/ddpg/ddpg.py import random import numpy as np import torch import torch.nn.functional as F import torch.optim as optim from model import ActorCritic device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class Agent(): def __init__(self): self.actor_critic_local = ActorCritic() self.actor_critic_target_local = ActorCritic() # hyperparameters self.gamma = .99 # discount factor self.tau = .001 # soft update parameter # optimizer self.optimizer_actor_local = optim.Adam(self.actor_critic_local.actor.parameters(), lr=1e-4) self.optimizer_actor_target_local = optim.Adam(self.actor_critic_target_local.actor.parameters(), lr=1e-4) self.optimizer_critic_local = optim.Adam(self.actor_critic_local.critic.parameters(), lr=1e-4) self.optimizer_critic_target_local = optim.Adam(self.actor_critic_target_local.critic.parameters(), lr=1e-4) def select_action(self): if __name__ == "__main__": env.reset() <|file_sep|># -*- coding: utf-8 -*- """ Created on Mon Jun-15-2020 @author: Aravind Gadde """ from collections import namedtuple import numpy as np import random from environment import Environment Transition=namedtuple('Transition', ('state', 'action', 'next_state', 'reward')) class ReplayMemory(object): def __init__(self,capacity): # assert type(capacity)==int,"capacity must be integer" # assert capacity>=0,"capacity must not less than zero" # assert type(window_size)==int,"window_size must be integer" # assert window_size>=0,"window_size must not less than zero" # assert window_size<=capacity,"window_size must not greater than capacity" # assert type(sample_length)==int,"sample_length must be integer" # assert sample_length>=0,"sample_length must not less than zero" # assert type(dropout_rate)==float,"dropout_rate must be float" # assert dropout_rate>=0,"dropout_rate must not less than zero" # assert dropout_rate<=1,"dropout_rate must not greater than one" <|repo_name|>aravindgadde/ReinforcementLearning<|file_sep|>/sql/model.py import random import numpy as np import torch.nn as nn class Actor(nn.Module): def __init__(self): super().__init__() """ Neural Network Model Structure: input_dim : input dimension output_dim : output dimension hidden_dim : hidden layer dimension n_hidden_layers : number of hidden layers dropout_rate : dropout rate init_method : weight initialization method """ input_dim=output_dim=hidden_dim=n_hidden_layers=dropout_rate=init_method=None """ Input Layer : input layer structure definition """ linear_layer_0_input_dim=input_dim linear_layer_0_output_dim=hidden_dim linear_layer_0_bias=True linear_layer_0=self.add_module('linear_layer_0', nn.Linear(linear_layer_0_input_dim, linear_layer_0_output_dim, bias=linear_layer_0_bias)) """ Hidden Layer : hidden layer structure definition """ class Critic(nn.Module): def __init__(self): super().__init__() """ Neural Network Model Structure: input_dim : input dimension output_dim : output dimension hidden_dim : hidden layer dimension n_hidden_layers : number of hidden layers dropout_rate : dropout rate init_method : weight initialization method """ input_dim=output_dim=hidden_dim=n_hidden_layers=dropout_rate=init_method=None """ Input Layer : input layer structure definition """