Train DDPG Agent to Control Sliding Robot
This example shows how to train a deep deterministic policy gradient (DDPG) agent to generate trajectories for a robot sliding without friction over a @D plane, modeled in Simulink®. For more information on DDPG agents, see Deep Deterministic Policy Gradient (DDPG) Agents (Reinforcement Learning Toolbox).
Flying Robot Model
The reinforcement learning environment for this example is a sliding robot with its initial condition randomized around a ring having a radius of 15 m. The orientation of the robot is also randomized. The robot has two thrusters mounted on the side of the body that are used to propel and steer the robot. The training goal is to drive the robot from its initial condition to the origin facing east.
Open the model.
mdl = "rlFlyingRobotEnv"; open_system(mdl)
Set the initial model state variables.
theta0 = 0; x0 = -15; y0 = 0;
Define the sample time
Ts and the simulation duration
Ts = 0.4; Tf = 30;
For this model:
The goal orientation is
0rad (robot facing east).
The thrust from each actuator is bounded from -1 to 1 N
The observations from the environment are the position, orientation (sine and cosine of orientation), velocity, and angular velocity of the robot.
The reward provided at every time step is
is the position of the robot along the x-axis.
is the position of the robot along the y-axis.
is the orientation of the robot.
is the control effort from the left thruster.
is the control effort from the right thruster.
is the reward when the robot is close to the goal.
is the penalty when the robot drives beyond 20 m in either the x or y direction. The simulation is terminated when .
is a QR penalty that penalizes distance from the goal and control effort.
Create Integrated Model
To train an agent for the
FlyingRobotEnv model, use the
createIntegratedEnv function to automatically generate a Simulink model containing an RL Agent block that is ready for training.
integratedMdl = "IntegratedFlyingRobot"; [~,agentBlk,obsInfo,actInfo] = ... createIntegratedEnv(mdl,integratedMdl);
Actions and Observations
Before creating the environment object, specify names for the observation and action specifications, and bound the thrust actions between -1 and 1.
The observation vector for this environment is . Assign a name to the environment observation channel.
obsInfo.Name = "observations";
The action vector for this environment is . Assign a name, as well as upper and lower limits, to the environment action channel.
actInfo.Name = "thrusts"; actInfo.LowerLimit = -ones(prod(actInfo.Dimension),1); actInfo.UpperLimit = ones(prod(actInfo.Dimension),1);
prod(actInfo.Dimension) return the number of dimensions of the observation and action spaces, respectively, regardless of whether they are arranged as row vectors, column vectors, or matrices.
Create Environment Object
Create an environment object using the integrated Simulink model.
env = rlSimulinkEnv( ... integratedMdl, ... agentBlk, ... obsInfo, ... actInfo);
Create a custom reset function that randomizes the initial position of the robot along a ring of radius 15 m and the initial orientation. For details on the reset function, see
env.ResetFcn = @(in) flyingRobotResetFcn(in);
Fix the random generator seed for reproducibility.
Create DDPG agent
DDPG agents use a parametrized Q-value function approximator to estimate the value of the policy. A Q-value function critic takes the current observation and an action as inputs and returns a single scalar as output (the estimated discounted cumulative long-term reward given the action from the state corresponding to the current observation, and following the policy thereafter).
To model the parametrized Q-value function within the critic, use a neural network with two input layers (one for the observation channel, as specified by
obsInfo, and the other for the action channel, as specified by
actInfo) and one output layer (which returns the scalar value).
Define each network path as an array of layer objects. Assign names to the input and output layers of each path. These names allow you to connect the paths and then later explicitly associate the network input and output layers with the appropriate environment channel.
% Specify the number of outputs for the hidden layers. hiddenLayerSize = 100; % Define observation path layers observationPath = [ featureInputLayer( ... prod(obsInfo.Dimension),Name="obsInLyr") fullyConnectedLayer(hiddenLayerSize) reluLayer fullyConnectedLayer(hiddenLayerSize) additionLayer(2,Name="add") reluLayer fullyConnectedLayer(hiddenLayerSize) reluLayer fullyConnectedLayer(1,Name="fc4") ]; % Define action path layers actionPath = [ featureInputLayer( ... prod(actInfo.Dimension), ... Name="actInLyr") fullyConnectedLayer(hiddenLayerSize,Name="fc5") ]; % Create the layer graph. criticNetwork = layerGraph(observationPath); criticNetwork = addLayers(criticNetwork,actionPath); % Connect actionPath to observationPath. criticNetwork = connectLayers(criticNetwork,"fc5","add/in2"); % Create dlnetwork from layer graph criticNetwork = dlnetwork(criticNetwork); % Display the number of parameters summary(criticNetwork)
Initialized: true Number of learnables: 21.4k Inputs: 1 'obsInLyr' 7 features 2 'actInLyr' 2 features
Create the critic using
criticNetwork, the environment specifications, and the names of the network input layers to be connected to the observation and action channels. For more information see
rlQValueFunction (Reinforcement Learning Toolbox).
critic = rlQValueFunction(criticNetwork,obsInfo,actInfo,... ObservationInputNames="obsInLyr",ActionInputNames="actInLyr");
DDPG agents use a parametrized deterministic policy over continuous action spaces, which is learned by a continuous deterministic actor. This actor takes the current observation as input and returns as output an action that is a deterministic function of the observation.
To model the parametrized policy within the actor, use a neural network with one input layer (which receives the content of the environment observation channel, as specified by
obsInfo) and one output layer (which returns the action to the environment action channel, as specified by
Define the network as an array of layer objects.
actorNetwork = [ featureInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(hiddenLayerSize) reluLayer fullyConnectedLayer(hiddenLayerSize) reluLayer fullyConnectedLayer(hiddenLayerSize) reluLayer fullyConnectedLayer(prod(actInfo.Dimension)) tanhLayer ];
Convert the array of layer object to a
dlnetwork object and display the number of parameters.
actorNetwork = dlnetwork(actorNetwork); summary(actorNetwork)
Initialized: true Number of learnables: 21.2k Inputs: 1 'input' 7 features
Define the actor using
actorNetwork, and the specifications for the action and observation channels. For more information, see
rlContinuousDeterministicActor (Reinforcement Learning Toolbox).
actor = rlContinuousDeterministicActor(actorNetwork,obsInfo,actInfo);
Specify options for the critic and the actor using
rlOptimizerOptions (Reinforcement Learning Toolbox).
criticOptions = rlOptimizerOptions(LearnRate=1e-03,GradientThreshold=1); actorOptions = rlOptimizerOptions(LearnRate=1e-04,GradientThreshold=1);
Specify the DDPG agent options using
rlDDPGAgentOptions (Reinforcement Learning Toolbox), include the training options for the actor and critic.
agentOptions = rlDDPGAgentOptions(... SampleTime=Ts,... ActorOptimizerOptions=actorOptions,... CriticOptimizerOptions=criticOptions,... ExperienceBufferLength=1e6 ,... MiniBatchSize=256); agentOptions.NoiseOptions.Variance = 1e-1; agentOptions.NoiseOptions.VarianceDecayRate = 1e-6;
Then, create the agent using the actor, the critic and the agent options. For more information, see
rlDDPGAgent (Reinforcement Learning Toolbox).
agent = rlDDPGAgent(actor,critic,agentOptions);
Alternatively, you can create the agent first, and then access its option object and modify the options using dot notation.
To train the agent, first specify the training options. For this example, use the following options:
Run each training for at most
20000episodes, with each episode lasting at most
Display the training progress in the Episode Manager dialog box (set the
Plotsoption) and disable the command line display (set the
Stop training when the agent receives an average cumulative reward greater than
415over 10 consecutive episodes. At this point, the agent can drive the sliding robot to the goal position.
Save a copy of the agent for each episode where the cumulative reward is greater than
For more information, see
rlTrainingOptions (Reinforcement Learning Toolbox).
maxepisodes = 20000; maxsteps = ceil(Tf/Ts); trainingOptions = rlTrainingOptions(... MaxEpisodes=maxepisodes,... MaxStepsPerEpisode=maxsteps,... StopOnError="on",... Verbose=false,... Plots="training-progress",... StopTrainingCriteria="AverageReward",... StopTrainingValue=415,... ScoreAveragingWindowLength=10,... SaveAgentCriteria="EpisodeReward",... SaveAgentValue=415);
Train the agent using the
train (Reinforcement Learning Toolbox) function. Training is a computationally intensive process that takes several hours to complete. To save time while running this example, load a pretrained agent by setting
false. To train the agent yourself, set
doTraining = false; if doTraining % Train the agent. trainingStats = train(agent,env,trainingOptions); else % Load the pretrained agent for the example. load("FlyingRobotDDPG.mat","agent") end
Simulate DDPG Agent
To validate the performance of the trained agent, simulate the agent within the environment. For more information on agent simulation, see
rlSimulationOptions (Reinforcement Learning Toolbox) and
sim (Reinforcement Learning Toolbox).
simOptions = rlSimulationOptions(MaxSteps=maxsteps); experience = sim(env,agent,simOptions);
- Train Reinforcement Learning Agents (Reinforcement Learning Toolbox)