From e0233faf88fdcfaf6a354f60e0b6bd08d60ece50 Mon Sep 17 00:00:00 2001
From: wassname <github@wassname.org>
Date: Thu, 6 Jun 2024 13:39:17 +0800
Subject: [PATCH] it learnt some acheivements, still takes a long time, and I
 might have made some params to small

---
 configs.yaml           |  49 +++-
 dreamer.py             |  14 +-
 envs/craftax_env.py    |   4 +-
 image.png              | Bin 0 -> 36554 bytes
 justfile               |   2 +-
 nbs/02_torchinfo.ipynb | 553 +++++++++++++++++++++++++++++++++++++++++
 networks.py            |   8 +-
 poetry.lock            |  13 +-
 pyproject.toml         |   1 +
 9 files changed, 628 insertions(+), 16 deletions(-)
 create mode 100644 image.png
 create mode 100644 nbs/02_torchinfo.ipynb

diff --git a/configs.yaml b/configs.yaml
index 03a46fa..2be2d69 100644
--- a/configs.yaml
+++ b/configs.yaml
@@ -62,7 +62,7 @@ defaults:
   initial: 'learned'
 
   # Training
-  batch_size: 256
+  batch_size: 64
   batch_length: 64
   train_ratio: 512
   pretrain: 100
@@ -136,18 +136,59 @@ craftax:
   action_repeat: 1
   envs: 1
   train_ratio: 512
-  video_pred_log: false # FIXME
+  video_pred_log: false
   dyn_hidden: 1024
   dyn_deter: 4096
   units: 1024
-  encoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 5, mlp_units: 1024, }
-  decoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 5, mlp_units: 1024}
+  encoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 4, mlp_units: 512, }
+  decoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 4, mlp_units: 512}
   actor: {layers: 5, dist: 'onehot', std: 'none'}
   value: {layers: 5}
   reward_head: {layers: 5}
   cont_head: {layers: 5}
   imag_gradient: 'reinforce'
 
+craftax_small:
+  task: craftax_Craftax-Symbolic-AutoReset-v1
+  step: 1e6
+  action_repeat: 1
+  envs: 1
+  train_ratio: 512
+  video_pred_log: false
+  dyn_hidden: 512
+  dyn_deter: 512
+  units: 512
+  encoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 3, mlp_units: 256}
+  decoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 3, mlp_units: 256}
+  actor: {layers: 3, dist: 'onehot', std: 'none'}
+  value: {layers: 3}
+  reward_head: {layers: 3}
+  cont_head: {layers: 3}
+  imag_gradient: 'reinforce'
+  batch_size: 128
+  batch_length: 16
+
+
+craftax_smaller:
+  task: craftax_Craftax-Symbolic-AutoReset-v1
+  step: 1e6
+  action_repeat: 1
+  envs: 1
+  train_ratio: 256
+  video_pred_log: false
+  dyn_hidden: 256
+  dyn_deter: 1024
+  units: 256
+  encoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 2, mlp_units: 256, }
+  decoder: {cnn_keys: '$^', mlp_keys: "state", mlp_layers: 2, mlp_units: 256}
+  actor: {layers: 2, dist: 'onehot', std: 'none'}
+  value: {layers: 2}
+  reward_head: {layers: 2}
+  cont_head: {layers: 2}
+  imag_gradient: 'reinforce'
+  batch_size: 256
+  batch_length: 16
+
 atari100k:
   steps: 4e5
   envs: 1
diff --git a/dreamer.py b/dreamer.py
index c8fa493..ed940e0 100644
--- a/dreamer.py
+++ b/dreamer.py
@@ -308,6 +308,7 @@ def main(config):
         agent.load_state_dict(checkpoint["agent_state_dict"])
         tools.recursively_load_optim_state_dict(agent, checkpoint["optims_state_dict"])
         agent._should_pretrain._once = False
+        logger.warning(f"Loaded model from {logdir / 'latest.pt'}")
 
     # make sure eval will be executed once after config.steps
     with tqdm(total=config.steps + config.eval_every, unit='step') as pbar:
@@ -356,13 +357,12 @@ def main(config):
         except Exception:
             pass
 
-
-if __name__ == "__main__":
+def parse_args(argv=None):
     parser = argparse.ArgumentParser()
     parser.add_argument("--configs", nargs="+")
-    args, remaining = parser.parse_known_args()
+    args, remaining = parser.parse_known_args(argv[1:])
     configs = yaml.safe_load(
-        (pathlib.Path(sys.argv[0]).parent / "configs.yaml").read_text()
+        (pathlib.Path(argv[0]).parent / "configs.yaml").read_text()
     )
 
     def recursive_update(base, update):
@@ -380,4 +380,8 @@ if __name__ == "__main__":
     for key, value in sorted(defaults.items(), key=lambda x: x[0]):
         arg_type = tools.args_type(value)
         parser.add_argument(f"--{key}", type=arg_type, default=arg_type(value))
-    main(parser.parse_args(remaining))
+    args = parser.parse_args(remaining)
+    return args
+
+if __name__ == "__main__":
+    main(parse_args())
diff --git a/envs/craftax_env.py b/envs/craftax_env.py
index 32fc068..2ea73cf 100644
--- a/envs/craftax_env.py
+++ b/envs/craftax_env.py
@@ -224,6 +224,8 @@ class Craftax:
     def step(self, action):
         state, reward, done, info = self._env.step(action)
 
+        info2 = {k.replace('Ach','log_ach'):v for k,v in info.items()}
+
         reward = np.float32(reward)
         obs = {
             "image": self.get_image(),
@@ -231,7 +233,7 @@ class Craftax:
             "is_first": False,
             "is_last": done,
             "is_terminal": info["discount"] == 0,
-            **info,
+            **info2,
         }
         return obs, reward, done, info
 
diff --git a/image.png b/image.png
new file mode 100644
index 0000000000000000000000000000000000000000..a57bec32a42228884c7052bfa89f725f9b4fbcfe
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diff --git a/justfile b/justfile
index ffc7f2c..c6c8b0a 100644
--- a/justfile
+++ b/justfile
@@ -6,7 +6,7 @@ export TQDM_MININTERVAL := "30"
 
 main:
   . ./.venv/bin/activate
-  python dreamer.py --configs crafter --task crafter_reward --logdir ./logdir/crafter
+  python dreamer.py --configs craftax_small --logdir ./logdir/crafter
   
 logs:
   tensorboard --logdir logdir/craftax 
diff --git a/nbs/02_torchinfo.ipynb b/nbs/02_torchinfo.ipynb
new file mode 100644
index 0000000..3e551c8
--- /dev/null
+++ b/nbs/02_torchinfo.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we load a saved dreamer, and run it, to look at params, speed and improve hackability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading textures from cache\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO make this a proper package\n",
+    "import os, sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "\n",
+    "from dreamer import parse_args, main, make_env, make_dataset, count_steps,Dreamer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['../dreamer.py', '--configs', 'craftax_small', '--logdir', '../logdir/craftax_small']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Namespace(act='SiLU', action_repeat=1, actor={'layers': 3, 'dist': 'onehot', 'entropy': 0.0003, 'unimix_ratio': 0.01, 'std': 'none', 'min_std': 0.1, 'max_std': 1.0, 'temp': 0.1, 'lr': 3e-05, 'eps': 1e-05, 'grad_clip': 100.0, 'outscale': 1.0}, batch_length=16, batch_size=128, compile=True, cont_head={'layers': 3, 'loss_scale': 1.0, 'outscale': 1.0}, critic={'layers': 2, 'dist': 'symlog_disc', 'slow_target': True, 'slow_target_update': 1, 'slow_target_fraction': 0.02, 'lr': 3e-05, 'eps': 1e-05, 'grad_clip': 100.0, 'outscale': 0.0}, dataset_size=1000000, debug=False, decoder={'mlp_keys': 'state', 'cnn_keys': '$^', 'act': 'SiLU', 'norm': True, 'cnn_depth': 32, 'kernel_size': 4, 'minres': 4, 'mlp_layers': 3, 'mlp_units': 256, 'cnn_sigmoid': False, 'image_dist': 'mse', 'vector_dist': 'symlog_mse', 'outscale': 1.0}, deterministic_run=False, device='cuda:0', disag_action_cond=False, disag_layers=4, disag_log=True, disag_models=10, disag_offset=1, disag_target='stoch', disag_units=400, discount=0.997, discount_lambda=0.95, dyn_deter=512, dyn_discrete=32, dyn_hidden=256, dyn_mean_act='none', dyn_min_std=0.1, dyn_rec_depth=1, dyn_scale=0.5, dyn_std_act='sigmoid2', dyn_stoch=32, encoder={'mlp_keys': 'state', 'cnn_keys': '$^', 'act': 'SiLU', 'norm': True, 'cnn_depth': 32, 'kernel_size': 4, 'minres': 4, 'mlp_layers': 3, 'mlp_units': 256, 'symlog_inputs': True}, envs=1, eval_episode_num=10, eval_every=10000.0, eval_state_mean=False, evaldir=None, expl_behavior='greedy', expl_extr_scale=0.0, expl_intr_scale=1.0, expl_until=0, grad_clip=1000, grad_heads=('decoder', 'reward', 'cont'), grayscale=False, imag_gradient='reinforce', imag_gradient_mix=0.0, imag_horizon=15, initial='learned', kl_free=1.0, log_every=10000.0, logdir='../logdir/craftax_small', model_lr=0.0001, norm=True, offline_evaldir='', offline_traindir='', opt='adam', opt_eps=1e-08, parallel=False, precision=32, prefill=2500, pretrain=100, rep_scale=0.1, reset_every=0, reward_EMA=True, reward_head={'layers': 3, 'dist': 'symlog_disc', 'loss_scale': 1.0, 'outscale': 0.0}, seed=0, size=(64, 64), step=1000000.0, steps=1000000.0, task='craftax_Craftax-Symbolic-AutoReset-v1', time_limit=1000, train_ratio=512, traindir=None, unimix_ratio=0.01, units=512, value={'layers': 3}, video_pred_log=False, weight_decay=0.0)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# emulate cli\n",
+    "argv = f\"../dreamer.py --configs craftax_small --logdir ../logdir/craftax_small\"\n",
+    "argv = argv.split()\n",
+    "print(argv)\n",
+    "config = parse_args(argv)\n",
+    "config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-06-06 13:35:50.147\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m27\u001b[0m - \u001b[1mLogdir ../logdir/craftax_small\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:35:50.153\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m36\u001b[0m - \u001b[1mCreate envs.\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:36:42.176\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m57\u001b[0m - \u001b[1mAction Space Box(0.0, 1.0, (43,), float32)\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:36:42.178\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m63\u001b[0m - \u001b[1mPrefill dataset (0 steps).\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:36:42.180\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m92\u001b[0m - \u001b[1mLogger: (128521 steps).\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:36:42.180\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m94\u001b[0m - \u001b[1mSimulate agent.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from loguru import logger\n",
+    "from tqdm.auto import tqdm\n",
+    "import pathlib\n",
+    "\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch import distributions as torchd\n",
+    "\n",
+    "import exploration as expl\n",
+    "import models\n",
+    "import tools\n",
+    "import envs.wrappers as wrappers\n",
+    "from parallel import Parallel, Damy\n",
+    "\n",
+    "# from main\n",
+    "tools.set_seed_everywhere(config.seed)\n",
+    "if config.deterministic_run:\n",
+    "    tools.enable_deterministic_run()\n",
+    "logdir = pathlib.Path(config.logdir).expanduser()\n",
+    "config.traindir = config.traindir or logdir / \"train_eps\"\n",
+    "config.evaldir = config.evaldir or logdir / \"eval_eps\"\n",
+    "config.steps //= config.action_repeat\n",
+    "config.eval_every //= config.action_repeat\n",
+    "config.log_every //= config.action_repeat\n",
+    "config.time_limit //= config.action_repeat\n",
+    "\n",
+    "logger.info(f\"Logdir {logdir}\")\n",
+    "logdir.mkdir(parents=True, exist_ok=True)\n",
+    "config.traindir.mkdir(parents=True, exist_ok=True)\n",
+    "config.evaldir.mkdir(parents=True, exist_ok=True)\n",
+    "step = count_steps(config.traindir)\n",
+    "# step in logger is environmental step\n",
+    "tlogger = tools.Logger(logdir, config.action_repeat * step)\n",
+    "logger.add(logdir/\"logger.log\")\n",
+    "\n",
+    "logger.info(\"Create envs.\")\n",
+    "if config.offline_traindir:\n",
+    "    directory = config.offline_traindir.format(**vars(config))\n",
+    "else:\n",
+    "    directory = config.traindir\n",
+    "train_eps = tools.load_episodes(directory, limit=config.dataset_size)\n",
+    "if config.offline_evaldir:\n",
+    "    directory = config.offline_evaldir.format(**vars(config))\n",
+    "else:\n",
+    "    directory = config.evaldir\n",
+    "eval_eps = tools.load_episodes(directory, limit=1)\n",
+    "make = lambda mode, id: make_env(config, mode, id)\n",
+    "train_envs = [make(\"train\", i) for i in range(config.envs)]\n",
+    "eval_envs = [make(\"eval\", i) for i in range(config.envs)]\n",
+    "if config.parallel:\n",
+    "    train_envs = [Parallel(env, \"process\") for env in train_envs]\n",
+    "    eval_envs = [Parallel(env, \"process\") for env in eval_envs]\n",
+    "else:\n",
+    "    train_envs = [Damy(env) for env in train_envs]\n",
+    "    eval_envs = [Damy(env) for env in eval_envs]\n",
+    "acts = train_envs[0].action_space\n",
+    "logger.info(f\"Action Space {acts}\" )\n",
+    "config.num_actions = acts.n if hasattr(acts, \"n\") else acts.shape[0]\n",
+    "\n",
+    "state = None\n",
+    "if not config.offline_traindir:\n",
+    "    prefill = max(0, config.prefill - count_steps(config.traindir))\n",
+    "    logger.info(f\"Prefill dataset ({prefill} steps).\")\n",
+    "    if hasattr(acts, \"discrete\"):\n",
+    "        random_actor = tools.OneHotDist(\n",
+    "            torch.zeros(config.num_actions).repeat(config.envs, 1)\n",
+    "        )\n",
+    "    else:\n",
+    "        random_actor = torchd.independent.Independent(\n",
+    "            torchd.uniform.Uniform(\n",
+    "                torch.Tensor(acts.low).repeat(config.envs, 1),\n",
+    "                torch.Tensor(acts.high).repeat(config.envs, 1),\n",
+    "            ),\n",
+    "            1,\n",
+    "        )\n",
+    "\n",
+    "    def random_agent(o, d, s):\n",
+    "        action = random_actor.sample()\n",
+    "        logprob = random_actor.log_prob(action)\n",
+    "        return {\"action\": action, \"logprob\": logprob}, None\n",
+    "\n",
+    "    state = tools.simulate(\n",
+    "        random_agent,\n",
+    "        train_envs,\n",
+    "        train_eps,\n",
+    "        config.traindir,\n",
+    "        tlogger,\n",
+    "        limit=config.dataset_size,\n",
+    "        steps=prefill,\n",
+    "    )\n",
+    "    tlogger.step += prefill * config.action_repeat\n",
+    "    logger.info(f\"Logger: ({tlogger.step} steps).\")\n",
+    "\n",
+    "logger.info(\"Simulate agent.\")\n",
+    "train_dataset = make_dataset(train_eps, config)\n",
+    "eval_dataset = make_dataset(eval_eps, config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-06-06 13:38:20.651\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnetworks\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m323\u001b[0m - \u001b[1mEncoder CNN shapes: {}\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.651\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnetworks\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m324\u001b[0m - \u001b[1mEncoder MLP shapes: {'state': (16536,)}\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.751\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnetworks\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mDecoder CNN shapes: {}\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.751\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnetworks\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m391\u001b[0m - \u001b[1mDecoder MLP shapes: {'state': (16536,)}\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.813\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodels\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mOptimizer model_opt has 15732120 variables.\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.836\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodels\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m281\u001b[0m - \u001b[1mOptimizer actor_opt has 1335851 variables.\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:20.837\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodels\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m292\u001b[0m - \u001b[1mOptimizer value_opt has 1181439 variables.\u001b[0m\n",
+      "\u001b[32m2024-06-06 13:38:21.032\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m17\u001b[0m - \u001b[33m\u001b[1mLoaded model from ../logdir/craftax_small/latest.pt\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "config = parse_args(argv)\n",
+    "config.num_actions = acts.n if hasattr(acts, \"n\") else acts.shape[0]\n",
+    "agent = Dreamer(\n",
+    "    train_envs[0].observation_space,\n",
+    "    train_envs[0].action_space,\n",
+    "    config,\n",
+    "    tlogger,\n",
+    "    train_dataset,\n",
+    ").to(config.device)\n",
+    "# print(agent)\n",
+    "agent.requires_grad_(requires_grad=False)\n",
+    "if (logdir / \"latest.pt\").exists():\n",
+    "    checkpoint = torch.load(logdir / \"latest.pt\")\n",
+    "    agent.load_state_dict(checkpoint[\"agent_state_dict\"])\n",
+    "    tools.recursively_load_optim_state_dict(agent, checkpoint[\"optims_state_dict\"])\n",
+    "    agent._should_pretrain._once = False\n",
+    "    logger.warning(f\"Loaded model from {logdir / 'latest.pt'}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now lets play"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 0, array([ True]), array([0], dtype=int32), [None], None, [0])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "assert state is not None\n",
+    "import numpy as np\n",
+    "\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tools import convert, add_to_cache\n",
+    "envs = train_envs\n",
+    "cache = train_eps\n",
+    "\n",
+    "step, episode = 0, 0\n",
+    "done = np.ones(len(envs), bool)\n",
+    "length = np.zeros(len(envs), np.int32)\n",
+    "obs = [None] * len(envs)\n",
+    "agent_state = None\n",
+    "reward = [0] * len(envs)\n",
+    "\n",
+    "indices = [index for index, d in enumerate(done) if d]\n",
+    "results = [envs[i].reset() for i in indices]\n",
+    "results = [r() for r in results]\n",
+    "for index, result in zip(indices, results):\n",
+    "    t = result.copy()\n",
+    "    t = {k: convert(v) for k, v in t.items()}\n",
+    "    # action will be added to transition in add_to_cache\n",
+    "    t[\"reward\"] = 0.0\n",
+    "    t[\"discount\"] = 1.0\n",
+    "    # initial state should be added to cache\n",
+    "    add_to_cache(cache, envs[index].id, t)\n",
+    "    # replace obs with done by initial state\n",
+    "    obs[index] = result\n",
+    "# step agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-06-06 13:38:34.000\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mtools\u001b[0m:\u001b[36mwrite\u001b[0m:\u001b[36m85\u001b[0m - \u001b[1m[128521] model_loss \u001b[31m22.2\u001b[0m\u001b[1m / model_grad_norm \u001b[31m14.4\u001b[0m\u001b[1m / state_loss \u001b[31m17.4\u001b[0m\u001b[1m / reward_loss \u001b[31m0.1\u001b[0m\u001b[1m / cont_loss \u001b[31m0.0\u001b[0m\u001b[1m / kl_free \u001b[31m1.0\u001b[0m\u001b[1m / dyn_scale \u001b[31m0.5\u001b[0m\u001b[1m / rep_scale \u001b[31m0.1\u001b[0m\u001b[1m / dyn_loss \u001b[31m7.8\u001b[0m\u001b[1m / rep_loss \u001b[31m7.8\u001b[0m\u001b[1m / kl \u001b[31m7.7\u001b[0m\u001b[1m / prior_ent \u001b[31m48.4\u001b[0m\u001b[1m / post_ent \u001b[31m40.7\u001b[0m\u001b[1m / normed_target_mean \u001b[31m0.4\u001b[0m\u001b[1m / normed_target_std \u001b[31m0.3\u001b[0m\u001b[1m / normed_target_min \u001b[31m-0.3\u001b[0m\u001b[1m / normed_target_max \u001b[31m1.8\u001b[0m\u001b[1m / EMA_005 \u001b[31m12.3\u001b[0m\u001b[1m / EMA_095 \u001b[31m26.4\u001b[0m\u001b[1m / value_mean \u001b[31m18.2\u001b[0m\u001b[1m / value_std \u001b[31m4.3\u001b[0m\u001b[1m / value_min \u001b[31m10.1\u001b[0m\u001b[1m / value_max \u001b[31m31.1\u001b[0m\u001b[1m / target_mean \u001b[31m18.4\u001b[0m\u001b[1m / target_std \u001b[31m4.7\u001b[0m\u001b[1m / target_min \u001b[31m8.4\u001b[0m\u001b[1m / target_max \u001b[31m37.8\u001b[0m\u001b[1m / imag_reward_mean \u001b[31m0.0\u001b[0m\u001b[1m / imag_reward_std \u001b[31m0.1\u001b[0m\u001b[1m / imag_reward_min \u001b[31m-0.2\u001b[0m\u001b[1m / imag_reward_max \u001b[31m1.0\u001b[0m\u001b[1m / imag_action_mean \u001b[31m10.0\u001b[0m\u001b[1m / imag_action_std \u001b[31m12.9\u001b[0m\u001b[1m / imag_action_min \u001b[31m0.0\u001b[0m\u001b[1m / imag_action_max \u001b[31m42.0\u001b[0m\u001b[1m / actor_entropy \u001b[31m0.9\u001b[0m\u001b[1m / actor_loss \u001b[31m0.1\u001b[0m\u001b[1m / actor_grad_norm \u001b[31m0.5\u001b[0m\u001b[1m / value_loss \u001b[31m1.3\u001b[0m\u001b[1m / value_grad_norm \u001b[31m0.9\u001b[0m\u001b[1m / update_count \u001b[31m1.0\u001b[0m\u001b[1m / fps \u001b[31m0.0\u001b[0m\u001b[1m\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from tools.simulate\n",
+    "\n",
+    "# step\n",
+    "# step, episode, done, length, obs, agent_state, reward = state\n",
+    "obs = {k: np.stack([o[k] for o in obs]) for k in obs[0] if \"log_\" not in k}\n",
+    "action, agent_state = agent(obs, done, agent_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "=====================================================================================\n",
+       "Layer (type:depth-idx)                                       Param #\n",
+       "=====================================================================================\n",
+       "Dreamer                                                      --\n",
+       "├─OptimizedModule: 1-1                                       --\n",
+       "│    └─WorldModel: 2-1                                       --\n",
+       "│    │    └─MultiEncoder: 3-1                                (4,365,824)\n",
+       "│    │    └─RSSM: 3-2                                        (3,831,808)\n",
+       "│    │    └─ModuleDict: 3-3                                  (7,534,488)\n",
+       "├─OptimizedModule: 1-2                                       --\n",
+       "│    └─ImagBehavior: 2-2                                     15,732,120\n",
+       "│    │    └─WorldModel: 3-4                                  (recursive)\n",
+       "│    │    └─MLP: 3-5                                         (1,335,851)\n",
+       "│    │    └─MLP: 3-6                                         (1,181,439)\n",
+       "│    │    └─MLP: 3-7                                         (1,181,439)\n",
+       "├─OptimizedModule: 1-3                                       (recursive)\n",
+       "│    └─ImagBehavior: 2-3                                     (recursive)\n",
+       "│    │    └─WorldModel: 3-8                                  (recursive)\n",
+       "│    │    └─MLP: 3-9                                         (recursive)\n",
+       "│    │    └─MLP: 3-10                                        (recursive)\n",
+       "│    │    └─MLP: 3-11                                        (recursive)\n",
+       "=====================================================================================\n",
+       "Total params: 35,162,969\n",
+       "Trainable params: 0\n",
+       "Non-trainable params: 35,162,969\n",
+       "====================================================================================="
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torchinfo import summary\n",
+    "\n",
+    "summary(agent, input=(obs, done, agent_state), depth=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fine grained torchinfo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wm = agent._wm\n",
+    "data = next(agent._dataset) \n",
+    "# self._train()\n",
+    "# post, context, mets = wm._train(data)\n",
+    "data = wm.preprocess(data)\n",
+    "embed = wm.encoder(data)\n",
+    "post, prior = wm.dynamics.observe(\n",
+    "    embed, data[\"action\"], data[\"is_first\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "==========================================================================================\n",
+       "Layer (type:depth-idx)                   Output Shape              Param #\n",
+       "==========================================================================================\n",
+       "MultiEncoder                             [128, 16, 256]            --\n",
+       "├─MLP: 1-1                               [128, 16, 256]            --\n",
+       "│    └─Sequential: 2-1                   [128, 16, 256]            --\n",
+       "│    │    └─Linear: 3-1                  [128, 16, 256]            (4,233,216)\n",
+       "│    │    └─LayerNorm: 3-2               [128, 16, 256]            (512)\n",
+       "│    │    └─SiLU: 3-3                    [128, 16, 256]            --\n",
+       "│    │    └─Linear: 3-4                  [128, 16, 256]            (65,536)\n",
+       "│    │    └─LayerNorm: 3-5               [128, 16, 256]            (512)\n",
+       "│    │    └─SiLU: 3-6                    [128, 16, 256]            --\n",
+       "│    │    └─Linear: 3-7                  [128, 16, 256]            (65,536)\n",
+       "│    │    └─LayerNorm: 3-8               [128, 16, 256]            (512)\n",
+       "│    │    └─SiLU: 3-9                    [128, 16, 256]            --\n",
+       "==========================================================================================\n",
+       "Total params: 4,365,824\n",
+       "Trainable params: 0\n",
+       "Non-trainable params: 4,365,824\n",
+       "Total mult-adds (M): 558.83\n",
+       "==========================================================================================\n",
+       "Input size (MB): 487.31\n",
+       "Forward/backward pass size (MB): 25.17\n",
+       "Params size (MB): 17.46\n",
+       "Estimated Total Size (MB): 529.94\n",
+       "=========================================================================================="
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary(wm.encoder, input_data=(data,), depth=3, col_names=[\"output_size\", \"num_params\", ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "decoder\n",
+      "==========================================================================================\n",
+      "Layer (type:depth-idx)                   Output Shape              Param #\n",
+      "==========================================================================================\n",
+      "MultiDecoder                             --                        --\n",
+      "├─MLP: 1-1                               --                        --\n",
+      "│    └─Sequential: 2-1                   [128, 16, 256]            --\n",
+      "│    │    └─Linear: 3-1                  [128, 16, 256]            (393,216)\n",
+      "│    │    └─LayerNorm: 3-2               [128, 16, 256]            (512)\n",
+      "│    │    └─SiLU: 3-3                    [128, 16, 256]            --\n",
+      "│    │    └─Linear: 3-4                  [128, 16, 256]            (65,536)\n",
+      "│    │    └─LayerNorm: 3-5               [128, 16, 256]            (512)\n",
+      "│    │    └─SiLU: 3-6                    [128, 16, 256]            --\n",
+      "│    │    └─Linear: 3-7                  [128, 16, 256]            (65,536)\n",
+      "│    │    └─LayerNorm: 3-8               [128, 16, 256]            (512)\n",
+      "│    │    └─SiLU: 3-9                    [128, 16, 256]            --\n",
+      "│    └─ModuleDict: 2-2                   --                        --\n",
+      "│    │    └─Linear: 3-10                 [128, 16, 16536]          (4,249,752)\n",
+      "==========================================================================================\n",
+      "Total params: 4,775,576\n",
+      "Trainable params: 0\n",
+      "Non-trainable params: 4,775,576\n",
+      "Total mult-adds (M): 611.27\n",
+      "==========================================================================================\n",
+      "Input size (MB): 12.58\n",
+      "Forward/backward pass size (MB): 296.09\n",
+      "Params size (MB): 19.10\n",
+      "Estimated Total Size (MB): 327.78\n",
+      "==========================================================================================\n",
+      "Summary Failed for reward Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [Sequential: 1, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 1]\n",
+      "Summary Failed for cont Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [Sequential: 1, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 2, LayerNorm: 2, SiLU: 2, Linear: 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heads\n",
+    "feat = wm.dynamics.get_feat(post)\n",
+    "for name, head in wm.heads.items():\n",
+    "    try:\n",
+    "        o = summary(head, input_data=(feat,), depth=3, col_names=[\"output_size\", \"num_params\", ])\n",
+    "        print(name)\n",
+    "        print(o)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Summary Failed for {name} {e}\")\n",
+    "        continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fail as no call method\n",
+    "# summary(wm.dynamics, input_data=(embed, data[\"action\"], data[\"is_first\"]), depth=3, col_names=[\"output_size\", \"num_params\", ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/networks.py b/networks.py
index 6f22d0e..7661f7f 100644
--- a/networks.py
+++ b/networks.py
@@ -320,8 +320,8 @@ class MultiEncoder(nn.Module):
             for k, v in shapes.items()
             if len(v) in (1, 2) and re.match(mlp_keys, k)
         }
-        logger.info("Encoder CNN shapes:", self.cnn_shapes)
-        logger.info("Encoder MLP shapes:", self.mlp_shapes)
+        logger.info("Encoder CNN shapes: {}", self.cnn_shapes)
+        logger.info("Encoder MLP shapes: {}", self.mlp_shapes)
 
         self.outdim = 0
         if self.cnn_shapes:
@@ -387,8 +387,8 @@ class MultiDecoder(nn.Module):
             for k, v in shapes.items()
             if len(v) in (1, 2) and re.match(mlp_keys, k)
         }
-        logger.info("Decoder CNN shapes: %s", self.cnn_shapes)
-        logger.info("Decoder MLP shapes: %s", self.mlp_shapes)
+        logger.info("Decoder CNN shapes: {}", self.cnn_shapes)
+        logger.info("Decoder MLP shapes: {}", self.mlp_shapes)
 
         if self.cnn_shapes:
             some_shape = list(self.cnn_shapes.values())[0]
diff --git a/poetry.lock b/poetry.lock
index d9ceae6..7cee8d2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3578,6 +3578,17 @@ type = "legacy"
 url = "https://download.pytorch.org/whl/cu121"
 reference = "pytorch"
 
+[[package]]
+name = "torchinfo"
+version = "1.8.0"
+description = "Model summary in PyTorch, based off of the original torchsummary."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "torchinfo-1.8.0-py3-none-any.whl", hash = "sha256:2e911c2918603f945c26ff21a3a838d12709223dc4ccf243407bce8b6e897b46"},
+    {file = "torchinfo-1.8.0.tar.gz", hash = "sha256:72e94b0e9a3e64dc583a8e5b7940b8938a1ac0f033f795457f27e6f4e7afa2e9"},
+]
+
 [[package]]
 name = "tornado"
 version = "6.4"
@@ -3836,4 +3847,4 @@ test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-it
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "8d04aef5b114f7ae76dc03bc61d308f1b239d390b0c71fab7d0c8f467cc95dd4"
+content-hash = "0275da73363d94f6a5cdadc9662c1b254ef50310aabce3aa663552aa4802b001"
diff --git a/pyproject.toml b/pyproject.toml
index f378589..5af5591 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ imageio = "^2.34.1"
 craftax = {path = "/media/wassname/SGIronWolf/projects5/2024/Craftax", develop = true }
 # craftax = {git = "https://github.com/wassname/Craftax" , develop = true } 
 chex = "^0.1.86"
+torchinfo = "^1.8.0"
 
 [tool.poetry.group.dev.dependencies]
 ipywidgets = "^8.1.3"