Files
simple_crypto/.ipynb_checkpoints/Test-checkpoint.ipynb

409 lines
166 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"id": "5ebc5698-7678-4730-8bdb-26d39cc3969d",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from pettingzoo.mpe import simple_reference_v3,simple_v3\n",
"import numpy as np\n",
"from IPython.display import clear_output\n",
"from IPython.core.debugger import set_trace\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "c1eaeab6-4b9e-4b5c-8db2-bd79b3887413",
"metadata": {},
"outputs": [],
"source": [
"max_frames = 5000000\n",
"batch_size = 5\n",
"learning_rate = 7e-4\n",
"gamma = 0.99\n",
"entropy_coef = 0.01\n",
"critic_coef = 0.5\n",
"no_of_workers = 16"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "5da2c091-9ac7-4142-803c-4e8b2b4c586e",
"metadata": {},
"outputs": [],
"source": [
"FloatTensor = torch.FloatTensor\n",
"LongTensor = torch.LongTensor\n",
"environment = simple_reference_v3"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "04f39958-d3f8-4484-a7fd-97f7da05cd4b",
"metadata": {},
"outputs": [],
"source": [
"class Model(torch.nn.Module):\n",
" def __init__(self, observation_space, action_space):\n",
" super(Model, self).__init__()\n",
" self.features = torch.nn.Sequential(\n",
" torch.nn.Linear(observation_space, 32),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(32, 128),\n",
" torch.nn.ReLU()\n",
" )\n",
"\n",
" self.critic = torch.nn.Sequential(\n",
" torch.nn.Linear(128, 256),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(256, 1)\n",
" )\n",
"\n",
" self.actor = torch.nn.Sequential(\n",
" torch.nn.Linear(128, 256),\n",
" torch.nn.ReLU(),\n",
" torch.nn.Linear(256, action_space),\n",
" torch.nn.Softmax(dim=-1)\n",
" )\n",
" \n",
" def forward(self, x):\n",
" x = self.features(x)\n",
" value = self.critic(x)\n",
" actions = self.actor(x)\n",
" return value, actions\n",
"\n",
" def get_critic(self, x):\n",
" x = self.features(x)\n",
" return self.critic(x)\n",
" \n",
" def evaluate_action(self, state, action):\n",
" value, actor_features = self.forward(state)\n",
" dist = torch.distributions.Categorical(actor_features)\n",
" log_probs = dist.log_prob(action).view(-1, 1)\n",
" entropy = dist.entropy().mean()\n",
"\n",
" return value, log_probs, entropy\n",
" \n",
" def act(self, state):\n",
" value, actor_features = self.forward(state)\n",
" dist = torch.distributions.Categorical(actor_features)\n",
"\n",
" chosen_action = dist.sample()\n",
" return chosen_action.item()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "58dcf7c9-6405-4d83-9ea5-bdebdd614db8",
"metadata": {},
"outputs": [],
"source": [
"class Memory(object):\n",
" def __init__(self):\n",
" self.states, self.actions, self.true_values = [], [], []\n",
" \n",
" def push(self, state, action, true_value):\n",
" self.states.append(state)\n",
" self.actions.append(action)\n",
" self.true_values.append(true_value)\n",
" \n",
" def pop_all(self):\n",
" states = torch.stack(self.states)\n",
" actions = LongTensor(self.actions)\n",
" true_values = FloatTensor(self.true_values).unsqueeze(1)\n",
"\n",
" self.states, self.actions, self.true_values = [], [], []\n",
" return states, actions, true_values"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "4edd19f6-193e-4183-9eab-498f176b32a2",
"metadata": {},
"outputs": [],
"source": [
"class Worker(object):\n",
" def __init__(self):\n",
" self.env = environment.parallel_env()\n",
" self.episode_rewards = {}\n",
" for agent in self.env.possible_agents:\n",
" self.episode_rewards[agent] = 0\n",
" self.state = self.env.reset()[0]\n",
"\n",
" def get_batch(self):\n",
" states, actions, rewards, dones = {}, {}, {}, {}\n",
" for agent in self.env.possible_agents:\n",
" states[agent] = []\n",
" actions[agent] = []\n",
" rewards[agent] = []\n",
" dones[agent] = []\n",
" for _ in range(batch_size):\n",
" actiondict = {}\n",
" for agent in self.env.agents:\n",
" mystate = FloatTensor(self.state[agent])\n",
" action = models[agent].act(mystate.unsqueeze(0))\n",
" actiondict[agent] = action\n",
" next_state, reward, terminations, truncations, _ = self.env.step(actiondict)\n",
" for agent in actiondict:\n",
" mystate = FloatTensor(self.state[agent])\n",
" self.episode_rewards[agent] += reward[agent]\n",
" states[agent].append(mystate)\n",
" actions[agent].append(actiondict[agent])\n",
" rewards[agent].append(reward[agent])\n",
" done = False if agent in self.env.agents else True\n",
" dones[agent].append(done)\n",
" \n",
" if not self.env.agents:\n",
" self.state = self.env.reset()[0]\n",
" for agent in self.episode_rewards:\n",
" data[agent]['episode_rewards'].append(self.episode_rewards[agent])\n",
" self.episode_rewards[agent] = 0\n",
" else:\n",
" self.state = next_state\n",
" values = {}\n",
" for agent in states:\n",
" values[agent] = compute_true_values(states[agent], rewards[agent], dones[agent], agent).unsqueeze(1)\n",
" return states, actions, values"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "a5bb4113-8511-437b-ae9d-1e9f45005073",
"metadata": {},
"outputs": [],
"source": [
"def compute_true_values(states, rewards, dones, agent):\n",
" true_values = []\n",
" rewards = FloatTensor(rewards)\n",
" dones = FloatTensor(dones)\n",
" states = torch.stack(states)\n",
"\n",
" if dones[-1] == True:\n",
" next_value = rewards[-1]\n",
" else:\n",
" next_value = models[agent].get_critic(states[-1].unsqueeze(0))\n",
"\n",
" true_values.append(next_value)\n",
" for i in reversed(range(0, len(rewards) -1)):\n",
" if not dones[i]:\n",
" next_value = rewards[i] + next_value * gamma\n",
" else:\n",
" next_value = rewards[i]\n",
" true_values.append(next_value)\n",
" true_values.reverse()\n",
" return FloatTensor(true_values)\n",
"\n",
"def reflect(memory, agent):\n",
" states, actions, true_values = memory.pop_all()\n",
" values, log_probs, entropy = models[agent].evaluate_action(states, actions)\n",
" advantages = true_values - values\n",
" critic_loss = advantages.pow(2).mean()\n",
" actor_loss = -(log_probs * advantages.detach()).mean()\n",
" total_loss = (critic_coef * critic_loss) + actor_loss - (entropy_coef * entropy)\n",
" optimizers[agent].zero_grad()\n",
" total_loss.backward()\n",
" torch.nn.utils.clip_grad_norm_(models[agent].parameters(), 0.5)\n",
" optimizers[agent].step()\n",
" return values.mean().item()\n",
"\n",
"def plot(data, frame_idx):\n",
" clear_output(True)\n",
" plt.figure(figsize=(20, 5))\n",
" #if data['episode_rewards']:\n",
" ax = plt.subplot(121)\n",
" ax = plt.gca()\n",
" plt.title(f\"Frame: {frame_idx}\")\n",
" plt.grid()\n",
" for agent in data:\n",
" average_score = np.mean(data[agent]['episode_rewards'][-100:])\n",
" plt.plot(data[agent]['episode_rewards'], label=f\"{agent} Avg: {average_score}\")\n",
" plt.legend()\n",
" #if data['values']:\n",
" ax = plt.subplot(122)\n",
" plt.title(f\"Frame: {frame_idx}\")\n",
" for agent in data:\n",
" average_value = np.mean(data[agent]['values'][-1000:])\n",
" plt.plot(data[agent]['values'], label=f\"{agent} Avg: {average_value}\")\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "3f0c10ca-c344-450d-ad78-bd418860ea5d",
"metadata": {},
"outputs": [],
"source": [
"env = environment.parallel_env()\n",
"models = {}\n",
"memories = {}\n",
"optimizers = {}\n",
"data = {}\n",
"for agent in env.possible_agents: \n",
" models[agent] = Model(env.observation_space(agent).shape[0], env.action_space(agent).n)\n",
" optimizers[agent] = torch.optim.RMSprop(models[agent].parameters(), lr=learning_rate, eps=1e-5)\n",
" memories[agent] = Memory()\n",
" data[agent] = {\n",
" 'episode_rewards': [],\n",
" 'values': []\n",
" }\n",
"workers = []\n",
"for _ in range(no_of_workers):\n",
" workers.append(Worker())\n",
"frame_idx = 0"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "1b3bae48-f47e-4b8b-be8a-271d7e6871c0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 2000x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[73]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m frame_idx < max_frames:\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m worker \u001b[38;5;129;01min\u001b[39;00m workers:\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m states, actions, true_values = \u001b[43mworker\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m agent \u001b[38;5;129;01min\u001b[39;00m states:\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i, _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(states[agent]):\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[64]\u001b[39m\u001b[32m, line 15\u001b[39m, in \u001b[36mWorker.get_batch\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 13\u001b[39m actions[agent] = []\n\u001b[32m 14\u001b[39m rewards[agent] = []\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m dones[agent] = []\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(batch_size):\n\u001b[32m 17\u001b[39m actiondict = {}\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
"source": [
"%debug\n",
"#state = FloatTensor(env.reset()[0][\"agent_0\"])\n",
"#episode_reward = 0\n",
"while frame_idx < max_frames:\n",
" for worker in workers:\n",
" states, actions, true_values = worker.get_batch()\n",
" for agent in states:\n",
" for i, _ in enumerate(states[agent]):\n",
" memories[agent].push(states[agent][i], actions[agent][i], true_values[agent][i])\n",
" frame_idx += batch_size\n",
" for agent in memories:\n",
" value = reflect(memories[agent], agent)\n",
" data[agent]['values'].append(value)\n",
" if frame_idx % 1000 == 0:\n",
" plot(data, frame_idx)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c6db61c0-6828-4283-855d-0d734b36a1fa",
"metadata": {},
"outputs": [],
"source": [
"torch.save(model, \"MyModel.pt\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9ef651cb-69d4-4c37-a92b-de5d4a9fe5a2",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 6\u001b[39m actiondict = {}\n\u001b[32m 7\u001b[39m actiondict[\u001b[33m\"\u001b[39m\u001b[33magent_0\u001b[39m\u001b[33m\"\u001b[39m] = action\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m next_state, reward, terminations, truncations, _ = \u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactiondict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m done = \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33magent_0\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m terminations \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m done:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/utils/conversions.py:207\u001b[39m, in \u001b[36maec_to_parallel_wrapper.step\u001b[39m\u001b[34m(self, actions)\u001b[39m\n\u001b[32m 203\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[32m 204\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mexpected agent \u001b[39m\u001b[38;5;132;01m{\u001b[39;00magent\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m got agent \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.aec_env.agent_selection\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, Parallel environment wrapper expects agents to step in a cycle.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 205\u001b[39m )\n\u001b[32m 206\u001b[39m obs, rew, termination, truncation, info = \u001b[38;5;28mself\u001b[39m.aec_env.last()\n\u001b[32m--> \u001b[39m\u001b[32m207\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43maec_env\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactions\u001b[49m\u001b[43m[\u001b[49m\u001b[43magent\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 208\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m agent \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.aec_env.agents:\n\u001b[32m 209\u001b[39m rewards[agent] += \u001b[38;5;28mself\u001b[39m.aec_env.rewards[agent]\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/utils/wrappers/order_enforcing.py:96\u001b[39m, in \u001b[36mOrderEnforcingWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m 94\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 95\u001b[39m \u001b[38;5;28mself\u001b[39m._has_updated = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m96\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/utils/wrappers/base.py:47\u001b[39m, in \u001b[36mBaseWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m 46\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action: ActionType) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/utils/wrappers/assert_out_of_bounds.py:26\u001b[39m, in \u001b[36mAssertOutOfBoundsWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action: ActionType) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[32m 18\u001b[39m action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 19\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[32m (...)\u001b[39m\u001b[32m 24\u001b[39m action\n\u001b[32m 25\u001b[39m ), \u001b[33m\"\u001b[39m\u001b[33maction is not in action space\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/utils/wrappers/base.py:47\u001b[39m, in \u001b[36mBaseWrapper.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m 46\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action: ActionType) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43menv\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/mpe/_mpe_utils/simple_env.py:264\u001b[39m, in \u001b[36mSimpleEnv.step\u001b[39m\u001b[34m(self, action)\u001b[39m\n\u001b[32m 261\u001b[39m \u001b[38;5;28mself\u001b[39m._accumulate_rewards()\n\u001b[32m 263\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.render_mode == \u001b[33m\"\u001b[39m\u001b[33mhuman\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m264\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/code/machine_learning/simple_crypto/venv/lib/python3.13/site-packages/pettingzoo/mpe/_mpe_utils/simple_env.py:287\u001b[39m, in \u001b[36mSimpleEnv.render\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 285\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.render_mode == \u001b[33m\"\u001b[39m\u001b[33mhuman\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 286\u001b[39m pygame.display.flip()\n\u001b[32m--> \u001b[39m\u001b[32m287\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtick\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrender_fps\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 288\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
"source": [
"env = simple_v3.parallel_env(render_mode=\"human\")\n",
"while True:\n",
" state = FloatTensor(env.reset()[0][\"agent_0\"])\n",
" while env.agents:\n",
" action = model.act(state.unsqueeze(0))\n",
" actiondict = {}\n",
" actiondict[\"agent_0\"] = action\n",
" next_state, reward, terminations, truncations, _ = env.step(actiondict)\n",
" done = False if \"agent_0\" in terminations else True\n",
"\n",
" if done:\n",
" state = FloatTensor(env.reset()[0][\"agent_0\"])\n",
" else:\n",
" state = FloatTensor(next_state[\"agent_0\"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4782cc0d-2180-4c50-990c-4814dbc1b595",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['agent_0']\n"
]
}
],
"source": [
"env = simple_v3.parallel_env()\n",
"print(env.possible_agents)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "machine_learning",
"language": "python",
"name": "machine_learning"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}