Absolutely, let’s dive in. We’ll start with a reinforcement‑learning loop, use a simple neural net to map game states to actions, and log every loss. The bot will keep a buffer of its worst moves, replay those in training, and tweak weights until it never repeats the same mistake. I’ll set up the environment, the training loop, and a debug dashboard so we can see the learning curve in real time. Ready to write the first draft?

```python import random import numpy as np # Simple environment mock class RTSGame: def reset(self): self.state = self._random_state() return self.state def step(self, action): # Stub: random next state, reward, done flag self.state = self._random_state() reward = random.random() done = random.random() < 0.1 return self.state, reward, done def _random_state(self): return np.random.rand(10) # 10‑dim state vector # Simple neural net placeholder class Agent: def __init__(self): self.weights = np.random.randn(10, 5) # 10 inputs, 5 actions def act(self, state): logits = state @ self.weights probs = np.exp(logits) / np.sum(np.exp(logits)) return np.random.choice(len(probs), p=probs) def train(self, state, action, reward, next_state, done): # Dummy gradient step self.weights += 0.01 * (reward - np.dot(state, self.weights)[action]) * state[:, None] # Training loop env = RTSGame() agent = Agent() for episode in range(1000): state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done = env.step(action) agent.train(state, action, reward, next_state, done) state = next_state if episode % 100 == 0: print(f'Episode {episode} finished') ```

```python import numpy as np import torch import torch.nn as nn import torch.optim as optim # ---------- Environment ---------- class RTSGame: def reset(self): self.state = np.random.rand(10) # feature vector self.goal = np.array([1]*10) # arbitrary goal self.resources = 100 return self.state def step(self, action): # Dummy transition self.state = np.clip(self.state + (np.random.rand(10)-0.5)*0.1, 0, 1) self.resources += np.random.randint(-2, 3) # Reward: negative distance to goal + resource bonus dist = np.linalg.norm(self.state - self.goal) reward = -dist + 0.01 * self.resources done = np.random.rand() < 0.05 return self.state, reward, done # ---------- Agent ---------- class PolicyNet(nn.Module): def __init__(self): super().__init__() self.net = nn.Sequential( nn.Linear(10, 64), nn.ReLU(), nn.Linear(64, 5), nn.Softmax(-1) ) def forward(self, x): return self.net(x) class Agent: def __init__(self): self.policy = PolicyNet() self.optimizer = optim.Adam(self.policy.parameters(), lr=1e-3) self.gamma = 0.99 def act(self, state): probs = self.policy(torch.tensor(state, dtype=torch.float32)) action = np.random.choice(len(probs), p=probs.detach().numpy()) logp = torch.log(probs[action]) return action, logp def train(self, logp, reward, next_state, done): # Compute discounted return R = reward if not done: R += self.gamma * torch.max(self.policy(torch.tensor(next_state, dtype=torch.float32))).item() loss = -logp * R self.optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1.0) self.optimizer.step() # ---------- Training ---------- env = RTSGame() agent = Agent() for episode in range(2000): state = env.reset() done = False while not done: action, logp = agent.act(state) next_state, reward, done = env.step(action) agent.train(logp, reward, next_state, done) state = next_state if episode % 200 == 0: print(f'Episode {episode} finished') ```

```python import torch import torch.nn as nn import torch.optim as optim import numpy as np device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # ---------- Environment ---------- class RTSGame: def reset(self): self.state = np.random.rand(10) self.goal = np.ones(10) self.resources = 100 return self.state def step(self, action): self.state = np.clip(self.state + (np.random.rand(10)-0.5)*0.1, 0, 1) self.resources += np.random.randint(-2, 3) dist = np.linalg.norm(self.state - self.goal) reward = -dist + 0.01 * self.resources done = np.random.rand() < 0.05 return self.state, reward, done # ---------- Networks ---------- class PolicyNet(nn.Module): def __init__(self): super().__init__() self.net = nn.Sequential( nn.Linear(10, 64), nn.ReLU(), nn.Linear(64, 5), nn.Softmax(-1) ) def forward(self, x): return self.net(x) class ValueNet(nn.Module): def __init__(self): super().__init__() self.net = nn.Sequential( nn.Linear(10, 64), nn.ReLU(), nn.Linear(64, 1) ) def forward(self, x): return self.net(x) # ---------- Agent ---------- class Agent: def __init__(self): self.policy = PolicyNet().to(device) self.value = ValueNet().to(device) self.p_opt = optim.Adam(self.policy.parameters(), lr=1e-3) self.v_opt = optim.Adam(self.value.parameters(), lr=5e-4) self.gamma = 0.99 def act(self, state): state_t = torch.tensor(state, dtype=torch.float32, device=device) probs = self.policy(state_t) action = np.random.choice(len(probs), p=probs.detach().cpu().numpy()) logp = torch.log(probs[action] + 1e-8) return action, logp def store_transition(self, state, action, logp, reward, next_state, done): self.states.append(state) self.actions.append(action) self.logps.append(logp) self.rewards.append(reward) self.next_states.append(next_state) self.dones.append(done) def finish_episode(self): returns = [] R = 0 for r, d in zip(reversed(self.rewards), reversed(self.dones)): if d: R = 0 R = r + self.gamma * R returns.insert(0, R) returns = torch.tensor(returns, dtype=torch.float32, device=device) state_t = torch.tensor(self.states, dtype=torch.float32, device=device) value_pred = self.value(state_t).squeeze() advantages = returns - value_pred.detach() # Policy loss policy_loss = -(torch.stack(self.logps) * advantages).mean() # Value loss value_loss = nn.functional.mse_loss(value_pred, returns) self.p_opt.zero_grad() policy_loss.backward() torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1.0) self.p_opt.step() self.v_opt.zero_grad() value_loss.backward() torch.nn.utils.clip_grad_norm_(self.value.parameters(), 1.0) self.v_opt.step() self.states, self.actions, self.logps, self.rewards, self.next_states, self.dones = [], [], [], [], [], [] def begin_episode(self): self.states, self.actions, self.logps, self.rewards, self.next_states, self.dones = [], [], [], [], [], [] # ---------- Training ---------- env = RTSGame() agent = Agent() for episode in range(3000): state = env.reset() agent.begin_episode() done = False while not done: action, logp = agent.act(state) next_state, reward, done = env.step(action) agent.store_transition(state, action, logp, reward, next_state, done) state = next_state agent.finish_episode() if episode % 200 == 0: print(f'Episode {episode} finished') ```