Expert Overview

Seattle Sounders and Sporting Kansas City are set to face off on August 25, 2025, at 01:15. This match promises to be a high-scoring affair, as indicated by the average total goals of 4.59. Both teams have shown a tendency to score, with Seattle Sounders having a strong likelihood of scoring in both halves, particularly in the second half. Sporting Kansas City, while slightly less likely to score in the first half, presents opportunities for late goals, especially after the 73rd minute.

Predictions

• Over 1.5 Goals: With an odds of 76.80, this is a strong bet given the teams’ attacking prowess.
• Home Team To Score In 2nd Half: At 77.20, Seattle Sounders are expected to capitalize on their home advantage and break through in the latter stages of the game.
• Over 0.5 Goals HT: Odds of 70.10 suggest an active first half with at least one goal scored.
• Under 5.5 Cards: A safer bet with odds of 73.10, indicating disciplined play with few bookings.
• Over 2.5 Goals: With odds of 69.30, this reflects the offensive capabilities of both teams.
• Home Team To Score In 1st Half: At 65.00, Seattle Sounders are likely to find the net early.
• Both Teams Not To Score In 2nd Half: Odds of 63.80 suggest a possibility of defensive consolidation as the game progresses.
• Both Teams To Score: With odds of 62.60, expect goals from both sides.
• Draw In First Half: At 58.60, a stalemate is possible before halftime.
• Home Team To Win: Odds of 62.50 favor Seattle Sounders clinching victory at home.
• Both Teams Not To Score In 1st Half: At 61.20, there’s potential for a slow start.
• Over 2.5 BTTS: Odds of 59.30 highlight frequent goal exchanges between both teams.
• Last Goal 73+ Minutes: At 60.80, expect a dramatic finish with goals scored late in the match.
• Over 1.5 Goals HT: With odds of 54.00, anticipate an engaging first half with multiple goals.
• Away Team Not To Score In First Half: Odds of 56.90 suggest Sporting Kansas City may struggle initially.
• Away Team Not To Score In Second Half: At 56.50, Seattle Sounders could effectively neutralize Kansas City’s attacks later in the game.
• Avg. Total Goals: Expected to be high at around 4.59.
• Avg. Yellow Cards: Projected at about 2.43, indicating moderate discipline levels on both sides.
• Avg. Goals Scored: Around 2.28 per team suggests offensive opportunities will be abundant.
• Avg. Conceded Goals: Approximately 1.81 per team indicates both defenses will be tested but should hold up reasonably well.
• Avg. Total Goals (Reiteration): Reinforces the expectation for a goal-rich match with over four goals anticipated overall.
• Last Goal Before Full Time (73+ Minutes): A potential game-changer with odds reflecting its likelihood at around 60 minutes or later into injury time.
• Away Team Not Scoring Early (Minute 0-29): With odds at 52.40, it’s likely that Sporting Kansas City may not score early on in the match.
• Average Total Goals (Reiteration): Further emphasizes expectations for a dynamic and high-scoring encounter.
• Average Yellow Cards (Reiteration): Supports an expectation for disciplined play throughout the match.
• Average Goals Scored (Reiteration): Highlights Seattle Sounders’ offensive potential to make significant impacts during the game.
• Average Conceded Goals (Reiteration): Indicates both teams will need to focus on defensive strategies despite their attacking tendencies.
• Average Total Goals (Final Note): Confirms anticipation for an exciting game with plenty of scoring opportunities for both sides involved.
• Average Yellow Cards (Final Note): Suggests that while there may be some bookings, neither side is expected to face excessive disciplinary action throughout the match duration.</vishalchopra22/ML_Agents-Environment/ML-Agents/MLAgents/UnityEnvironment.cs
  using System;
  using System.Collections.Generic;
  using System.IO;
  using System.Threading;
  using System.Threading.Tasks;
  using UnityEngine;

  namespace MLAgents
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  public class UnityEnvironment : IEnvironment
  #else
  public class UnityEnvironment : IEnvironmentManager
  #endif
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  // For editor purposes only
  [SerializeField] private GameObject m_environmentGameObject;
  #endif

  private int m_maxStep = -1;
  private bool m_isDone = false;

  private List[] m_agentInfosActions;

  private bool m_isInitialized = false;
  private bool m_enableAutomaticStepping = true;

  public bool IsInitialized => m_isInitialized;

  public void Initialize(VectorObservationWriter vectorObservationWriter,
  ActionObservationWriter actionObservationWriter,
  VectorActionSpace vectorActionSpace,
  ActionSpec[] actionSpecs,
  BrainParameters brainParameters)
  {
  m_isInitialized = true;
  }

  public void Reset()
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  if (!m_environmentGameObject)
  throw new UnityAgentsException(“Could not find environment GameObject.”);
  #endif

  m_isDone = false;

  #if UNITY_EDITOR || !UNITY_STANDALONE
  m_environmentGameObject.GetComponent().Reset();
  #endif

  m_agentInfosActions = new List[actionSpecs.Length];
  for (var i = 0; i != actionSpecs.Length; ++i)
  m_agentInfosActions[i] = new List();
  }

  public void SetMaxStep(int maxStep)
  {
  m_maxStep = maxStep;
  }

  public void SetAutomaticStepping(bool automaticStepping)
  {
  m_enableAutomaticStepping = automaticStepping;
  }

  public void SetExternalStepping(bool externalStepping)
  {
  if (!m_enableAutomaticStepping)
  throw new UnityAgentsException(“Trying to set external stepping when automatic stepping is disabled.”);
  #if UNITY_EDITOR || !UNITY_STANDALONE
  m_environmentGameObject.GetComponent().SetExternalStepping(externalStepping);
  #endif
  }

  public void Step(ActionBatch actions)
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  if (!m_environmentGameObject)
  throw new UnityAgentsException(“Could not find environment GameObject.”);

  if (!m_enableAutomaticStepping && !actions.externalStep) {
  throw new UnityAgentsException(“Attempting to step with automatic stepping disabled and without externalStep flag set”);
  }
  #endif

  if (m_isDone)
  {
  Debug.LogWarning(“Attempted step while environment was done.”);
  return;
  }

  for (var i = actions.agentActions.Length -1; i >=0; –i) {
  var agentActionList = actions.agentActions[i];
  if (agentActionList != null) {
  if (agentActionList.Count ==0) {
  Debug.LogWarning(“No actions provided for agent ” + i);
  } else {
  foreach(var agentAction in agentActionList) {
  m_agentInfosActions[i].Add(new AgentInfoActionPair(agentAction.AgentId,
  agentAction.StepsFromDone,
  agentAction.Action));
  }
  }
  }
  }

  if (!m_enableAutomaticStepping && actions.externalStep) {
  return;
  }

  #if UNITY_EDITOR || !UNITY_STANDALONE
  m_environmentGameObject.GetComponent().Step();
  #endif

  if (m_maxStep >=0 && m_maxStep <= Time.frameCount) {
  m_isDone = true;
  }

  #if UNITY_EDITOR || !UNITY_STANDALONE
  m_environmentGameObject.GetComponent().SetExternalStepping(false);
  #endif

  if (!m_enableAutomaticStepping) {
  return;
  }

  #if UNITY_EDITOR || !UNITY_STANDALONE
  while (!m_environmentGameObject.GetComponent().IsStepComplete()) {
  Thread.Sleep(0);
  }
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE
  // The environment is updated by now.
  #endif

  foreach(var agentInfoActions in m_agentInfosActions) {
  agentInfoActions.Clear();
  }

  #if UNITY_EDITOR || !UNITY_STANDALONE
  if(m_environmentGameObject.GetComponent().IsDone()) {
  m_isDone = true;
  }
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE
  // TODO: check if this can be removed once we remove all non-environment updates from Update().
  // TODO: Update() is called twice every frame when running editor tests.
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE
  m_environmentGameObject.GetComponent().Update();
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE
  // Update() might not have been called yet.
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE
  if(m_environmentGameObject.GetComponent().IsDone()) {
  m_isDone = true;
  }
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE && PLATFORM_WINSTORE && PLATFORM_64BIT_BUILD
  // TODO: Enable this once we fix https://github.com/Unity-Technologies/ml-agents/issues/2058.
  // This can’t be enabled right now because it causes hangs during unit tests.
  #else
  // On certain platforms like iOS and Android there might be multiple frames between steps so we need to update multiple times here.
  while(!IsDone()) {
  Thread.Sleep(0);
  Update();
  }
  #endif

  #if UNITY_EDITOR || !UNITY_STANDALONE && PLATFORM_WINSTORE && PLATFORM_64BIT_BUILD
  // TODO: Enable this once we fix https://github.com/Unity-Technologies/ml-agents/issues/2058.
  // This can’t be enabled right now because it causes hangs during unit tests.
  #else
  // Make sure we update one more time just in case there was a frame between this and the last call.
  Update();
  #endif
  }

  public AgentInfo[] CollectMetrics()
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  var metrics = new AgentInfo[actionSpecs.Length];
  var unityEnvComp = m_environmentGameObject.GetComponent();

  for(var i=0; i!=metrics.Length; ++i) {
  metrics[i] = new AgentInfo(i,
  unityEnvComp.GetAgentMetric(i).Reward,
  unityEnvComp.GetAgentMetric(i).TotalSteps,
  unityEnvComp.GetAgentMetric(i).LocalSteps);
  }

  return metrics;
  #else
  throw new UnityAgentsException(“This method is only available when running inside Unity.”);
  #endif
  }

  public void CollectObservations(VectorSensor sensor)
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  var unityEnvComp = m_environmentGameObject.GetComponent();

  unityEnvComp.CollectObservations(sensor.Id);
  #else
  throw new UnityAgentsException(“This method is only available when running inside Unity.”);
  #endif
  }

  public void CollectDiscreteActions(DiscreteActionSensor sensor)
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  var unityEnvComp = m_environmentGameObject.GetComponent();

  unityEnvComp.CollectDiscreteActions(sensor.Id);
  #else
  throw new UnityAgentsException(“This method is only available when running inside Unity.”);
  #endif
  }

  public void CollectVectorActions(VectorActionSensor sensor)
  {
  #if UNITY_EDITOR || !UNITY_STANDALONE
  var unityEnvComp = m_environmentGameObject.GetComponent();

  unityEnvComp.CollectVectorActions(sensor.Id);
  #else
  throw new UnityAgentsException(“This method is only available when running inside Unity.”);
  #endif
  }

  public AgentInfo[] GetResults(Nullable[] rewards,
  Nullable[] dones,
  Nullable[] observations,
  Nullable discreteActionMasks,
  Nullable[] continuousActions)
  {

  #ifdef DEBUG_LOGGING
  Debug.Log(“GetResults() called”);
  #endif

  var resultInfos = new AgentInfo[actionSpecs.Length];
  for(var i=0; i!=resultInfos.Length; ++i) {
  resultInfos[i] = new AgentInfo(i);
  }

  foreach(var infosAndActions in m_agentInfosActions) {
  foreach(var pair in infosAndActions) {
  var infoAndActionIndex= pair.AgentId;

  resultInfos[infoAndActionIndex].Reward += rewards[infoAndActionIndex].GetValueOrDefault(0f);

  resultInfos[infoAndActionIndex].Done |= dones[infoAndActionIndex].GetValueOrDefault(false);

  observations[infoAndActionIndex] =
  new VectorSensorInformation(pair.StepsFromDone.GetValueOrDefault(0),
  pair.Observation);

  discreteActionMasks[infoAndActionIndex] =
  pair.DiscreteMask.GetValueOrDefault(DiscreteActionMask.Default);

  continuousActions[infoAndActionIndex] =
  pair.ContinuousAction.GetValueOrDefault(VectorAction.Default);
  }
  }

  return resultInfos;

  }
  }
  # ML-Agents Environment

  This repository contains code that allows you to use ML-Agents environments outside of Unity.

  ## Installing

  To install from source:

  git clone https://github.com/Unity-Technologies/ml-agents.git ML-Agents –recursive –branch main –depth=1

  cd ML-Agents/ML-Agents-Env/

  pip install .

  To install from PyPi:

  pip install ml-agents-env==0.*

  ## Using Environments

  You can use environments either by building them yourself or by using prebuilt environments.

  ### Building Environments Yourself

  Building your own environment requires you to build it using CMake and then write Python bindings using pybind11.

  For example:

  cd /path/to/your/environment/
  mkdir build && cd build/
  cmake -G “Visual Studio” ..
  cmake –build . –config Release –target mlagents_env_bindings_pybind11 — -maxcpucount:4 -verbosity:minimal -restore -property:GenerateManifest=false -property:UseSharedCompilation=false -property:ContinuousIntegrationBuild=true -property:TreatWarningsAsErrors=false -property:PlatformToolset=v142 -property:WindowsTargetPlatformVersion=10 -property:VCToolset=v142
  cmake –build . –config Release –target mlagents_env_bindings_python_install — -maxcpucount:4 -verbosity:minimal -restore -property:GenerateManifest=false -property:UseSharedCompilation=false -property:ContinuousIntegrationBuild=true -property:TreatWarningsAsErrors=false -property:PlatformToolset=v142 -property:WindowsTargetPlatformVersion=10 -property:VCToolset=v142

  python setup.py build_ext –inplace # If you are developing bindings and want them built immediately instead of doing so manually with CMake.

  The last line isn’t strictly required but can be helpful during development.

  After building your environment you will have an `env` directory containing `unity_env.py` which has your Python bindings and `libenv.so` which contains your compiled environment.

  Then you can create a Python script that imports `unity_env` from your `env` directory and uses it:

  python3
  from env import unity_env # Importing from our env directory created above.

  # Creating our environment.
  env_name=”test”
  my_env=unity_env.UnityEnv(env_name)

  # Resetting our environment before starting training.
  my_env.reset()

  # Starting training loop.
  while True:

  # Getting observations from our environment.
  obs=my_env.get_observations()

  # Creating our action space based on our observations.
  action_space=my_env.get_action_space()

  # Choosing actions based on our observations and action space.
  actions=choose_actions(observation=obs,action_space=action_space)

  # Sending our actions back to our environment.
  my_env.set_actions(actions)

  ### Using Prebuilt Environments

  You can also use prebuilt environments which are available through pip.

  For example:

  python3

  from mlagents.envs import UnityEnvironment # Importing our prebuilt environment.

  # Creating our environment.
  env_name=”test”
  my_env=UnityEnvironment(env_name)

  # Resetting our environment before starting training.
  my_env.reset()

  # Starting training loop.
  while True:

  # Getting observations from our environment.
  obs=my_env.get_observations()

  # Creating our action space based on our observations.
  action_space=my_env.get_action_space()

  # Choosing actions based on our observations and action space.
  actions=choose_actions(observation=obs