diff --git a/mjx/training_apg.ipynb b/mjx/training_apg.ipynb index c11f931c..512e6329 100644 --- a/mjx/training_apg.ipynb +++ b/mjx/training_apg.ipynb @@ -59,9 +59,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "---\n", + "\n", "**Coming Up**\n", "\n", - "In this tutorial, we demonstrate two ways to use FoPG's, using Brax's simple APG [algorithm](https://github.com/google/brax/tree/main/brax/training/agents/apg). This algorithm uses FoPG's to essentially perform live stochastic gradient descent on the policy, unrolling it for a short window, doing a policy update, then continuing where it left off." + "In this tutorial, we demonstrate two ways to use FoPG's, using Brax's simple APG [algorithm](https://github.com/google/brax/tree/main/brax/training/agents/apg). This algorithm essentially uses FoPG's to perform live gradient descent on the policy, unrolling it for a short window, using the data to do a policy update, then continuing where it left off." ] }, { @@ -90,7 +92,7 @@ "config.update(\"jax_debug_nans\", True)\n", "config.update(\"jax_enable_x64\", True)\n", "config.update('jax_default_matmul_precision', jax.lax.Precision.HIGH)\n", - "import matplotlib.pyplot as plt\n", + "from brax import math\n", "\n", "# Sim\n", "import mujoco\n", @@ -98,8 +100,6 @@ "\n", "# Brax\n", "from brax import envs\n", - "from brax import envs\n", - "from brax import math\n", "from brax.base import Motion, Transform\n", "from brax.io import mjcf\n", "from brax.envs.base import PipelineEnv, State\n", @@ -115,6 +115,7 @@ "# Supporting\n", "from etils import epath\n", "import mediapy as media\n", + "import matplotlib.pyplot as plt\n", "from ml_collections import config_dict\n", "from typing import Any, Dict\n" ] @@ -128,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -137,13 +138,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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" ], "text/plain": [ "" @@ -173,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -239,6 +240,8 @@ "def make_kinematic_ref(sinusoid, step_k, scale=0.3, dt=1/50):\n", " \"\"\" \n", " Makes trotting kinematics for the 12 leg joints.\n", + " step_k is the number of timesteps it takes to raise and lower a given foot.\n", + " A gait cycle is 2 * step_k * dt seconds long.\n", " \"\"\"\n", " \n", " _steps = jp.arange(step_k)\n", @@ -246,43 +249,45 @@ " t = _steps * dt\n", " \n", " wave = sinusoid(t, step_period, scale)\n", - " f_leg_cmd_bloc = jp.concatenate(\n", + " # Commands for one step of an active front leg\n", + " fleg_cmd_block = jp.concatenate(\n", " [jp.zeros((step_k, 1)),\n", " wave.reshape(step_k, 1),\n", " -2*wave.reshape(step_k, 1)],\n", " axis=1\n", " )\n", - " h_leg_cmd_bloc = -1 * f_leg_cmd_bloc\n", + " # Our standing config reverses front and hind legs\n", + " h_leg_cmd_bloc = -1 * fleg_cmd_block\n", "\n", - " bloc1 = jp.concatenate([\n", + " block1 = jp.concatenate([\n", " jp.zeros((step_k, 3)),\n", - " f_leg_cmd_bloc,\n", + " fleg_cmd_block,\n", " h_leg_cmd_bloc,\n", " jp.zeros((step_k, 3))],\n", " axis=1\n", " )\n", "\n", - " bloc2 = jp.concatenate([\n", - " f_leg_cmd_bloc,\n", + " block2 = jp.concatenate([\n", + " fleg_cmd_block,\n", " jp.zeros((step_k, 3)),\n", " jp.zeros((step_k, 3)),\n", " h_leg_cmd_bloc],\n", " axis=1\n", " )\n", - "\n", - " cycle = jp.concatenate([bloc1, bloc2], axis=0)\n", - " return cycle\n" + " # In one step cycle, both pairs of active legs have inactive and active phases\n", + " step_cycle = jp.concatenate([block1, block2], axis=0)\n", + " return step_cycle\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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" ], "text/plain": [ "" @@ -318,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +335,7 @@ " dict(\n", " min_reference_tracking = -2.5 * 3e-3, # to equalize the magnitude\n", " reference_tracking = -1.0,\n", - " feet_height = -1\n", + " feet_height = -1.0\n", " )\n", " )\n", " )\n", @@ -384,7 +389,6 @@ " 'n_frames', physics_steps_per_control_step)\n", "\n", " mj_model = mujoco.MjModel.from_xml_path(xml_path)\n", - " mj_model.opt.timestep = 0.002\n", " kp = 230\n", " mj_model.actuator_gainprm[:, 0] = kp\n", " mj_model.actuator_biasprm[:, 1] = -kp\n", @@ -555,7 +559,7 @@ " obs_list.append(state_info['last_action'])\n", " # kinematic reference\n", " kin_ref = self.kinematic_ref_qpos[jp.array(state_info['steps']%self.l_cycle, int)]\n", - " obs_list.append(kin_ref) # Gait schedule, per actuator.\n", + " obs_list.append(kin_ref[7:]) # Gait schedule, per actuator.\n", "\n", " obs = jp.clip(jp.concatenate(obs_list), -100.0, 100.0)\n", "\n", @@ -601,11 +605,11 @@ " Using minimal coordinates. Improves accuracy of joint angle tracking.\n", " \"\"\"\n", " pos = jp.concatenate([\n", - " state.pipeline_state.qpos[0:3],\n", - " state.pipeline_state.qpos[4:]])\n", + " state.pipeline_state.qpos[:3],\n", + " state.pipeline_state.qpos[7:]])\n", " pos_targ = jp.concatenate([\n", - " ref_qpos[0:3],\n", - " ref_qpos[4:]])\n", + " ref_qpos[:3],\n", + " ref_qpos[7:]])\n", " pos_err = jp.linalg.norm(pos_targ - pos)\n", " vel_err = jp.linalg.norm(state.pipeline_state.qvel- ref_qvel)\n", "\n", @@ -627,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -636,7 +640,7 @@ " hidden_layer_sizes=(256, 128)\n", ")\n", "\n", - "epochs = 500\n", + "epochs = 499\n", "\n", "train_fn = functools.partial(apg.train,\n", " episode_length=240,\n", @@ -653,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -662,13 +666,13 @@ "" ] }, - "execution_count": 9, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -702,13 +706,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ "" @@ -728,7 +732,8 @@ " jax.jit(demo_env.step),\n", " jax.jit(make_inference_fn(params)),\n", " demo_env,\n", - " n_steps=200\n", + " n_steps=200,\n", + " seed=1\n", ")\n", "\n", "model_path = '/tmp/trotting_2hz_policy'\n", @@ -838,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -875,7 +880,7 @@ "\n", " return default_config\n", "\n", - "class DiffAnymal(PipelineEnv):\n", + "class FwdTrotAnymal(PipelineEnv):\n", "\n", " def __init__(\n", " self,\n", @@ -892,6 +897,9 @@ " self.termination_height = termination_height\n", "\n", " mj_model = mujoco.MjModel.from_xml_path(xml_path)\n", + " kp = 230\n", + " mj_model.actuator_gainprm[:, 0] = kp\n", + " mj_model.actuator_biasprm[:, 1] = -kp\n", " self._init_q = mj_model.keyframe('standing').qpos\n", " self._default_ap_pose = mj_model.keyframe('standing').qpos[7:]\n", " self.reward_config = get_config()\n", @@ -911,12 +919,7 @@ " kinematic_ref_qpos = make_kinematic_ref(\n", " cos_wave, step_k, scale=0.3, dt=self.dt)\n", " self.l_cycle = jp.array(kinematic_ref_qpos.shape[0])\n", - "\n", - " # Expand to base state\n", - " kinematic_ref_qpos += self._default_ap_pose\n", - " ref_qs = np.tile(self._init_q.reshape(1, 19), (self.l_cycle, 1))\n", - " ref_qs[:, 7:] = kinematic_ref_qpos\n", - " self.kinematic_ref_qpos = jp.array(ref_qs)\n", + " self.kinematic_ref_qpos = jp.array(kinematic_ref_qpos + self._default_ap_pose)\n", "\n", " \"\"\"\n", " Foot tracking\n", @@ -1129,9 +1132,9 @@ " obs_list.append(angles - self._default_ap_pose)\n", " # last action\n", " obs_list.append(state_info['last_action'])\n", - " # base action\n", - " cur_base = self.kinematic_ref_qpos[jp.array(state_info['steps']%self.l_cycle, int)]\n", - " obs_list.append(cur_base)\n", + " # gait schedule\n", + " kin_ref = self.kinematic_ref_qpos[jp.array(state_info['steps']%self.l_cycle, int)]\n", + " obs_list.append(kin_ref)\n", "\n", " obs = jp.clip(jp.concatenate(obs_list), -100.0, 100.0)\n", "\n", @@ -1187,7 +1190,7 @@ " errs = jp.clip(jp.square(feet_height - h_tars), 0, 10)\n", " return jp.sum(errs)\n", " \n", - "envs.register_environment('anymal', DiffAnymal)\n" + "envs.register_environment('anymal', FwdTrotAnymal)\n" ] }, { @@ -1200,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1247,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1256,13 +1259,13 @@ "" ] }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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