Design RL with continuous output

Hi, I've been stucked in my project for several months. I am trying to apply RL to my project where I have two scalar and one vector. I have a single continious output with a single UAV. Do you have any exampke to help me to solve my problem. Thanks.

Answer 1

[Shubham]

Hi Alen,

To apply reinforcement learning (RL) with continuous output to your UAV project, here’s a concise guide to help you move forward:

1. Environment Setup:

• Define the state space: This could include UAV parameters such as position, velocity, and orientation.
• Action space: Since your output is continuous (e.g., thrust or torque), choose an RL agent that supports continuous actions.
• Reward function: Create a reward function that incentivizes your UAV to maintain stability or reach a target efficiently.

2. Choose an RL Algorithm:

• DDPG (Deep Deterministic Policy Gradient) for continuous control.
• TD3 (Twin-Delayed DDPG) reduces overestimation bias.
• PPO (Proximal Policy Optimization) provides more stable performance with continuous actions.

3. Agent Design:

• Use an Actor network to map states to actions.
• Design a Critic network to evaluate state-action pairs.
• You can create these networks with simple fully connected layers and refine them based on performance.

4. Training:

• Use a Simulink environment where the UAV's dynamics are simulated, allowing the agent to interact and learn. Here's a sample code using DDPG:

% Load the Simulink environment for UAV model
env = rlSimulinkEnv('UAVModel', 'UAVModel/RLAgent');

% Set the options for the DDPG agent
agentOptions = rlDDPGAgentOptions('SampleTime', 0.1, 'DiscountFactor', 0.99);

% Create the Actor and Critic networks (you'll need to define these)
% Example: actorNetwork = some predefined deep neural network
%          criticNetwork = another predefined deep neural network

% Create the DDPG agent using Actor and Critic networks
agent = rlDDPGAgent(actorNetwork, criticNetwork, agentOptions);

% Set the training options
trainOpts = rlTrainingOptions('MaxEpisodes', 500, 'MaxStepsPerEpisode', 200);

% Train the agent using the Simulink environment
train(agent, env, trainOpts);

5. Evaluation and Tuning:

• After training, evaluate your agent’s performance and adjust hyperparameters (like learning rate or network structure) to optimize results.

For more details on "Reinforcement Learning Toolbox", refer to the following MathWorks documentation link:

https://mathworks.com/help/releases/R2023b/reinforcement-learning/index.html

Hope this helps!