Robotics Algorithms

Q-Learning

Q-Learning is a model-free reinforcement learning algorithm to learn the value of actions in a state-action space.

# Q-Learning Algorithm Example in Python import numpy as np def q_learning(env, num_episodes, learning_rate, gamma, epsilon): Q = np.zeros([env.observation_space.n, env.action_space.n]) for i in range(num_episodes): state = env.reset() done = False while not done: action = np.argmax(Q[state, :] + np.random.randn(1, env.action_space.n) * (1.0 / (i + 1))) next_state, reward, done, _ = env.step(action) Q[state, action] = Q[state, action] + learning_rate * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action]) state = next_state return Q

Deep Q-Network (DQN)

DQN combines Q-Learning with deep learning by using a neural network to approximate the Q-values for reinforcement learning problems.

# DQN Example Pseudocode initialize replay memory and Q-network for each episode: initialize state for each step: choose action using epsilon-greedy policy perform action, get next state and reward store transition in replay memory sample random batch from replay memory perform gradient descent on the batch

Proximal Policy Optimization (PPO)

PPO is a policy gradient method that provides a balance between sample efficiency and ease of implementation for reinforcement learning tasks.

# PPO Pseudocode initialize actor and critic networks for each episode: for each timestep in the environment: collect states, actions, and rewards calculate advantage estimates using the critic update the actor network using policy gradient update the critic network using value loss

Deep Deterministic Policy Gradient (DDPG)

DDPG is an actor-critic method used for continuous action spaces, employing deterministic policies and Q-learning.

# DDPG Algorithm Pseudocode initialize actor and critic networks initialize replay buffer for each episode: for each step: select action with noise for exploration store transition in replay buffer sample random batch from buffer update actor and critic networks using gradient descent

Simultaneous Localization and Mapping (SLAM)

SLAM is the process by which a robot constructs a map of an unknown environment while simultaneously keeping track of its location.

# SLAM - General Workflow 1. Sense the environment using sensors (e.g., LIDAR, Camera) 2. Perform scan matching or feature detection 3. Use algorithms like EKF or Particle Filter to localize 4. Update the map with the new observations

Rapidly-Exploring Random Tree (RRT)

RRT is a path planning algorithm designed for efficiently navigating robots through high-dimensional spaces.

# RRT Algorithm Pseudocode initialize tree with starting position for each iteration: sample random point in the space find nearest node in the tree to the point steer towards the point if the path is valid, add it to the tree

Genetic Algorithm

Genetic algorithms simulate the process of natural selection to generate solutions to optimization and search problems.

# Genetic Algorithm Pseudocode initialize population with random solutions for each generation: evaluate fitness of population select parents based on fitness crossover parents to create new offspring mutate offspring randomly replace the least fit solutions with new offspring

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