PID code and Update Readme

README.md

@@ -179,6 +179,11 @@ installation and are specific to models of type
 We are planning to extend this in the future; if you have useful suggestions
 don't hesitate to raise an issue or submit a pull request!
 
+## Advanced Examples
+MBRL-Lib can be used for many different research projects in the subject area. 
+We have support for the following projects:
+*  [Trajectory-based Dynamics Model](https://arxiv.org/abs/2012.09156) Training [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/natolambert/mbrl-lib-dev/blob/main/notebooks/traj_based_model.ipynb) 
+
 ## Documentation 
 Please check out our **[documentation](https://facebookresearch.github.io/mbrl-lib/)** 
 and don't hesitate to raise issues or contribute if anything is unclear!

mbrl/planning/__init__.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 from .core import Agent, RandomAgent, complete_agent_cfg, load_agent
+from .linear_feedback import PIDAgent
 from .trajectory_opt import (
     CEMOptimizer,
     ICEMOptimizer,

mbrl/planning/linear_feedback.py

@@ -0,0 +1,122 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from typing import Optional
+
+import numpy as np
+
+from .core import Agent
+
+
+class PIDAgent(Agent):
+    """
+    Agent that reacts via an internal set of proportional–integral–derivative controllers.
+
+    A broad history of the PID controller can be found here:
+    https://en.wikipedia.org/wiki/PID_controller.
+
+    Args:
+        k_p (np.ndarry): proportional control coeff (Nx1)
+        k_i (np.ndarry): integral control coeff     (Nx1)
+        k_d (np.ndarry): derivative control coeff   (Nx1)
+        target (np.ndarry): setpoint                (Nx1)
+        state_mapping (np.ndarry): indices of the state vector to apply the PID control to.
+            E.g. for a system with states [angle, angle_vel, position, position_vel], state_mapping
+            of [1, 3] and dim of 2 will apply the PID to angle_vel and position_vel variables.
+        batch_dim (int): number of samples to compute actions for simultaneously
+    """
+
+    def __init__(
+        self,
+        k_p: np.ndarray,
+        k_i: np.ndarray,
+        k_d: np.ndarray,
+        target: np.ndarray,
+        state_mapping: Optional[np.ndarray] = None,
+        batch_dim: Optional[int] = 1,
+    ):
+        super().__init__()
+        assert len(k_p) == len(k_i) == len(k_d) == len(target)
+        self.n_dof = len(k_p)
+
+        # State mapping defaults to first N states
+        if state_mapping is not None:
+            assert len(state_mapping) == len(target)
+            self.state_mapping = state_mapping
+        else:
+            self.state_mapping = np.arange(0, self.n_dof)
+
+        self.batch_dim = batch_dim
+
+        self._prev_error = np.zeros((self.n_dof, self.batch_dim))
+        self._cum_error = np.zeros((self.n_dof, self.batch_dim))
+
+        self.k_p = np.repeat(k_p[:, np.newaxis], self.batch_dim, axis=1)
+        self.k_i = np.repeat(k_i[:, np.newaxis], self.batch_dim, axis=1)
+        self.k_d = np.repeat(k_d[:, np.newaxis], self.batch_dim, axis=1)
+        self.target = np.repeat(target[:, np.newaxis], self.batch_dim, axis=1)
+
+    def act(self, obs: np.ndarray, **_kwargs) -> np.ndarray:
+        """Issues an action given an observation.
+
+        This method optimizes a given observation or batch of observations for a
+            one-step action choice.
+
+
+        Args:
+            obs (np.ndarray): the observation for which the action is needed either N x 1 or N x B,
+                where N is the state dim and B is the batch size.
+
+        Returns:
+            (np.ndarray): the action outputted from the PID, either shape n_dof x 1 or n_dof x B.
+        """
+        if obs.ndim == 1:
+            obs = np.expand_dims(obs, -1)
+        if len(obs) > self.n_dof:
+            pos = obs[self.state_mapping]
+        else:
+            pos = obs
+
+        error = self.target - pos
+        self._cum_error += error
+
+        P_value = np.multiply(self.k_p, error)
+        I_value = np.multiply(self.k_i, self._cum_error)
+        D_value = np.multiply(self.k_d, (error - self._prev_error))
+        self._prev_error = error
+        action = P_value + I_value + D_value
+        return action
+
+    def reset(self):
+        """
+        Reset internal errors.
+        """
+        self._prev_error = np.zeros((self.n_dof, self.batch_dim))
+        self._cum_error = np.zeros((self.n_dof, self.batch_dim))
+
+    def get_errors(self):
+        return self._prev_error, self._cum_error
+
+    def _get_P(self):
+        return self.k_p
+
+    def _get_I(self):
+        return self.k_i
+
+    def _get_D(self):
+        return self.k_d
+
+    def _get_targets(self):
+        return self.target
+
+    def get_parameters(self):
+        """
+        Returns the parameters of the PID agent concatenated.
+
+        Returns:
+            (np.ndarray): the parameters.
+        """
+        return np.stack(
+            (self._get_P(), self._get_I(), self._get_D(), self._get_targets())
+        ).flatten()

tests/core/test_planning.py

@@ -0,0 +1,81 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import pytest
+
+import mbrl.planning as planning
+
+
+def create_pid_agent(dim,
+                     state_mapping=None,
+                     batch_dim=1):
+    agent = planning.PIDAgent(k_p=np.ones(dim, ),
+                              k_i=np.ones(dim, ),
+                              k_d=np.ones(dim, ),
+                              target=np.zeros(dim, ),
+                              state_mapping=state_mapping,
+                              batch_dim=batch_dim,
+                              )
+    return agent
+
+
+def test_pid_agent_one_dim():
+    """
+    This test covers the creation of PID agents in the most basic form.
+    """
+    pid = create_pid_agent(dim=1)
+    pid.reset()
+    init_obs = np.array([2.2408932])
+    act = pid.act(init_obs)
+
+    # check action computation
+    assert act == pytest.approx(-6.722, 0.1)
+
+    # check reset
+    pid.reset()
+    prev_error, cum_error = pid.get_errors()
+    assert np.sum(prev_error) == np.sum(cum_error) == 0
+
+
+def test_pid_agent_multi_dim():
+    """
+    This test covers regular updates for the multi-dim PID agent.
+    """
+    pid = create_pid_agent(dim=2, state_mapping=np.array([1, 3]), )
+    init_obs = np.array([ 0.95008842, -0.15135721, -0.10321885,  0.4105985 ])
+    act1 = pid.act(init_obs)
+    next_obs = np.array([0.14404357, 1.45427351, 0.76103773, 0.12167502])
+    act2 = pid.act(next_obs)
+    assert act1 + act2 == pytest.approx([-3.908, -1.596], 0.1)
+
+    # check reset
+    pid.reset()
+    prev_error, cum_error = pid.get_errors()
+    assert np.sum(prev_error) == np.sum(cum_error) == 0
+
+
+def test_pid_agent_batch(batch_dim=5):
+    """
+    Tests the agent for batch-mode computation of actions.
+    """
+    pid = create_pid_agent(dim=2, state_mapping=np.array([1, 3]), batch_dim=batch_dim)
+
+    init_obs = np.array([[ 0.95008842, -0.15135721, -0.10321885,  0.4105985 ,  0.14404357],
+       [ 1.45427351,  0.76103773,  0.12167502,  0.44386323,  0.33367433],
+       [ 1.49407907, -0.20515826,  0.3130677 , -0.85409574, -2.55298982],
+       [ 0.6536186 ,  0.8644362 , -0.74216502,  2.26975462, -1.45436567]])
+    act1 = pid.act(init_obs)
+    next_obs = np.array([[ 0.04575852, -0.18718385,  1.53277921,  1.46935877,  0.15494743],
+       [ 0.37816252, -0.88778575, -1.98079647, -0.34791215,  0.15634897],
+       [ 1.23029068,  1.20237985, -0.38732682, -0.30230275, -1.04855297],
+       [-1.42001794, -1.70627019,  1.9507754 , -0.50965218, -0.4380743 ]])
+    act2 = pid.act(next_obs)
+    assert (act1 + act2)[0] == pytest.approx([-5.497, 0.380, 5.577, -0.287, -1.470], 0.1)
+
+    # check reset
+    pid.reset()
+    prev_error, cum_error = pid.get_errors()
+    assert np.sum(prev_error) == np.sum(cum_error) == 0