Unveiling the Thrills of the Football Community Shield England
The Football Community Shield, often hailed as the curtain-raiser to the English football season, stands as a beacon of excitement and anticipation for fans across South Africa and beyond. As we gear up for fresh matches that promise to set the tone for what’s to come, this prestigious clash between the Premier League champions and the FA Cup winners never fails to captivate. With each passing year, the Community Shield intensifies its allure, offering not just a glimpse into the potential of the upcoming season but also a stage for new talents to shine. As local enthusiasts in South Africa, we bring you a deep dive into the latest matches, expert betting predictions, and everything you need to stay ahead in this thrilling football spectacle.
Understanding the Significance of the Community Shield
The Football Community Shield is more than just a match; it’s a tradition steeped in history and prestige. Established in 1908, it marks the beginning of the football calendar in England. This pre-season fixture pits the reigning Premier League champions against the FA Cup winners, offering a unique blend of rivalry and celebration. For South African fans, it’s an opportunity to witness top-tier English football before the official season kicks off.
- Historical Context: Understanding its roots helps appreciate its current stature.
- Competitive Edge: Teams often use this match to test new strategies and players.
- Fan Engagement: It serves as a prelude to the intense football season ahead.
Latest Matches: A Glimpse into Tomorrow’s Legends
Each day brings fresh matches, offering insights into emerging talents and tactical evolutions. South African fans have much to look forward to as these matches unfold. Whether it’s a seasoned veteran showcasing their prowess or a young prodigy making their mark, every game is an opportunity to witness football at its finest.
- Match Highlights: Key moments from recent games that defined outcomes.
- Player Performances: Standout individuals who are setting trends.
- Tactical Analysis: How teams are adapting to modern football dynamics.
Expert Betting Predictions: Your Guide to Smart Wagering
Betting on football can be as thrilling as watching the game itself. With expert predictions tailored for South African audiences, you can make informed decisions that enhance your experience. Our analysis delves into team form, head-to-head records, and player conditions to provide you with insights that go beyond mere speculation.
- Predictive Models: Advanced algorithms that analyze past performances.
- Betting Strategies: Tips on maximizing returns while minimizing risks.
- In-Depth Reports: Comprehensive reviews of each team’s strengths and weaknesses.
The Cultural Impact: Football Beyond Borders
Football transcends geographical boundaries, uniting people through shared passion and excitement. In South Africa, where football is deeply ingrained in the cultural fabric, events like the Community Shield resonate profoundly. They offer a platform for local communities to engage with global football narratives, fostering a sense of belonging and camaraderie.
- Social Gatherings: How matches become focal points for community interaction.
- Cultural Exchange: The role of football in promoting international understanding.
- Youth Inspiration: Encouraging young talents through global role models.
Tactical Breakdown: What Makes Each Team Tick
Understanding team tactics is crucial for appreciating the nuances of football. Each team approaches the Community Shield with distinct strategies aimed at gaining an upper hand. From defensive solidity to attacking flair, these tactical choices shape the course of each match.
- Formation Analysis: How different formations influence gameplay.
- Midfield Dynamics: The role of midfielders in controlling tempo.
- Defensive Strategies: Techniques used to thwart opposition attacks.
The Role of Technology: Enhancing Fan Experience
Technology has revolutionized how fans engage with football. From live streaming platforms to interactive apps, South African fans have unprecedented access to every facet of the game. This digital transformation enhances fan experience by providing real-time updates, expert commentary, and immersive content.
- Digital Platforms: The best apps and websites for live updates.
- Social Media Engagement: Connecting with fellow fans globally.
- Virtual Reality: Experiencing matches from unique perspectives.
Economic Impact: The Financial Side of Football
The economic implications of events like the Community Shield are significant. They generate substantial revenue through ticket sales, broadcasting rights, and merchandise. For South African businesses involved in sports tourism or related industries, these events present lucrative opportunities.
- Ticket Sales: Trends in attendance and pricing strategies.
- Broadcasting Rights: The financial dynamics behind television deals.
- Sponsorship Deals: How brands leverage high-profile matches for exposure.
Sustainability Initiatives: Football’s Green Future
tianlongze/SuperMarioAI<|file_sep|>/envs/super_mario_env.py
import sys
sys.path.append("../")
import os
import gym
import numpy as np
from PIL import Image
from gym import error, spaces
from gym.utils import seeding
from gym.envs.classic_control import rendering
# from .mario import MarioGame
class SuperMarioEnv(gym.Env):
# def __init__(self):
# self.mario_game = MarioGame()
# def _step(self, action):
# reward = self.mario_game.frame_step(action)
# done = False
# if self.mario_game.game_over():
# done = True
# return self.mario_game.get_state(), reward, done
# def _reset(self):
# self.mario_game.reset()
# return self.mario_game.get_state()
# def _render(self):
# pass
# def _close(self):
# pass
def main():
if __name__ == '__main__':
<|repo_name|>tianlongze/SuperMarioAI<|file_sep|>/model/mariomlp.py
import torch
import torch.nn as nn
class MarioMLP(nn.Module):
def __init__(self,
num_inputs=4,
num_outputs=4,
hidden_dim=64,
hidden_layers=2,
log_std_min=-20,
log_std_max=2):
super().__init__()
self.log_std_min = log_std_min
self.log_std_max = log_std_max
self.hidden_layers = nn.ModuleList([nn.Linear(num_inputs if i ==0 else hidden_dim,
hidden_dim) for i in range(hidden_layers)])
self.mu_layer = nn.Linear(hidden_dim,num_outputs)
self.log_std_layer = nn.Linear(hidden_dim,num_outputs)
def forward(self,x):
for layer in self.hidden_layers:
x = torch.tanh(layer(x))
mu = self.mu_layer(x)
log_std = self.log_std_layer(x)
log_std = torch.clamp(log_std,self.log_std_min,self.log_std_max)
return mu ,log_std<|file_sep|># Super Mario AI
An implementation of SAC algorithm using [OpenAI's Retro](https://github.com/openai/retro) environment.
## Dependencies:
1. python >= 3.6
2. pip install -r requirements.txt
## Setup:
To run this project you need:
1. Download [retro_contest.bin](https://github.com/openai/retro/blob/master/data/ROMS/snes/Super%20Mario%20World%20-%20USA.zip?raw=true) from OpenAI's retro repository.
2. Copy retro_contest.bin into data/ROMS/snes directory.
## Training:
To train model run following command:
python train.py --env SuperMarioWorld-Nes --seed 0 --num-timesteps 20000000 --lr 0.0005 --hidden-dim 512 --batch-size 256 --replay-size 1000000 --actor-critic repeat=2 --save-interval=1000000
## Testing:
To test model run following command:
python train.py --env SuperMarioWorld-Nes --seed 0 --num-timesteps -1 --load-path ./models/mario_0.pt --save-interval=1000000
## Results:
#### No Replay Buffer
#### Replay Buffer
### Additional results can be found here:
[](https://colab.research.google.com/drive/1P-hFVjZo4VXgYkOQk9r8uLqQFmO7YJpe#scrollTo=_rUWf-K7bupP)
<|repo_name|>tianlongze/SuperMarioAI<|file_sep|>/model/mariocritic.py
import torch
import torch.nn as nn
class MarioCritic(nn.Module):
def __init__(self,
num_inputs=4,
hidden_dim=64,
hidden_layers=2):
super().__init__()
self.hidden_layers = nn.ModuleList([nn.Linear(num_inputs if i ==0 else hidden_dim,
hidden_dim) for i in range(hidden_layers)])
self.v_layer = nn.Linear(hidden_dim ,1)
def forward(self,x):
for layer in self.hidden_layers:
x = torch.relu(layer(x))
v = self.v_layer(x)
return v<|repo_name|>tianlongze/SuperMarioAI<|file_sep|>/requirements.txt
gym==0.17.2
numpy==1.19.5
torch==1.8
matplotlib==3.4
tensorboard==2.5
scipy==1.7
tqdm==4.62
retro==1.13
opencv-python==4.5
gym-retro==0.9<|file_sep|># %%
import gym
import retro
env=gym.make("SuperMarioWorld-Nes")
env.reset()
obs_shape=env.observation_space.shape[0]
action_space_size=len(env.action_space)
print(obs_shape)
print(action_space_size)
# %%
# %%
obs=env.reset()
done=False
while not done:
env.render()
action=[True,False,False,False,False,False,False,False]
obs,reward,done,_=env.step(action)
print(obs.shape)
# %%
# %%
import numpy as np
def preprocess_frame(frame):
frame=np.mean(frame,axis=2).astype(np.uint8)
frame[frame!=0]=255
return frame
# %%
obs=env.reset()
done=False
while not done:
env.render()
action=[True,False,False,False,False,False,False,False]
frame,_ ,done,_=env.step(action)
frame=preprocess_frame(frame)
print(frame.shape)
# %%
# %%
from collections import deque
def stack_frames(stacked_frames ,state ,is_new_episode ,stack_size):
if is_new_episode:
stacked_frames=deque([np.zeros(state.shape) for i in range(stack_size)], maxlen=stack_size)
stacked_frames.append(state)
state=np.stack(stacked_frames,axis=-1)
return stacked_frames,state
# %%
stack_size=4
stacked_frames=None
state=None
obs=env.reset()
done=False
while not done:
env.render()
action=[True,False,False,False,False,False,False,False]
frame,reward ,done,_=env.step(action)
frame=preprocess_frame(frame)
if stacked_frames is None:
stacked_frames,state = stack_frames(stacked_frames ,frame ,True ,stack_size)
else:
stacked_frames,state = stack_frames(stacked_frames ,frame ,False ,stack_size)
print(state.shape)
# %%
obs_shape=(84,84,4)
def preprocess_frame(frame):
frame=np.mean(frame,axis=2).astype(np.uint8)
frame[frame!=0]=255
return frame
def stack_frames(stacked_frames ,state ,is_new_episode ,stack_size):
if is_new_episode:
stacked_frames=deque([np.zeros(state.shape) for i in range(stack_size)], maxlen=stack_size)
stacked_frames.append(state)
state=np.stack(stacked_frames,axis=-1)
return stacked_frames,state
stack_size=4
stacked_frames=None
obs=env.reset()
done=False
while not done:
env.render()
action=[True,False,False,False,False,False,False,False]
frame,reward ,done,_=env.step(action)
frame=preprocess_frame(frame).reshape(84,-1).astype(np.float32)/255
if stacked_frames is None:
stacked_frames,state = stack_frames(stacked_frames ,frame ,True ,stack_size)
else:
stacked_frames,state = stack_frames(stacked_frames ,frame ,False ,stack_size)
state=np.reshape(state,(84,-1)).astype(np.float32)/255
print(state.shape)
# %%
# %%
from collections import deque
import random
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
self.memory=[]
self.replay_buffer_size=replay_buffer_size
self.batch_size=batch_size
def add_to_memory(self,state ,action,reward,next_state,is_done):
self.memory.append((state,action,reward,next_state,is_done))
def sample_from_memory(self,batch_size=None):
if batch_size is None:
batch_size=self.batch_size
memory_len=len(self.memory)
batch=random.sample(self.memory,batch_size=min(memory_len,batch_size))
state_batch=[i[0] for i in batch]
action_batch=[i[1] for i in batch]
reward_batch=[i[2] for i in batch]
next_state_batch=[i[3] for i in batch]
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
self.memory=[]
self.replay_buffer_size=replay_buffer_size
self.batch_size=batch_size
def add_to_memory(self,state ,action,reward,next_state,is_done):
self.memory.append((state,np.array(action),reward,next_state,is_done))
def sample_from_memory(self,batch_size=None):
if batch_size is None:
batch_size=self.batch_size
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
self.memory=[]
self.replay_buffer_size=replay_buffer_size
self.batch_size=batch_size
def add_to_memory(self,state ,action,reward,next_state,is_done):
self.memory.append((state,np.array(action),reward,next_state,is_done))
def sample_from_memory(self,batch_size=None):
if batch_size is None:
batch_size=self.batch_size
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
self.memory=[]
self.replay_buffer_size=replay_buffer_size
self.batch_size=batch_size
def add_to_memory(self,state ,action,reward,next_state,is_done):
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
def __init__(self,replay_buffer_size,batch_size):
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
class ReplayBuffer():
# %%
replay_buffer=replay_buffer(10000)
# %%
# %%
from collections import deque
import random
import numpy as np
replay_buffer=replay_buffer(10000)
<|repo_name|>stefanlindberg/samplesite<|file_sep|>/source/_posts/2016-03-12-new-blog.md
---
title: New blog!
date: '2016-03-12'
tags:
- blog
categories:
-