Controlling a double-jointed robotic arm using Actor-Critic’s DDPG reinforcement learning algorithm

In this article, we will introduce training an intelligent agent to control a dual-jointed robotic arm in the Reacher environment, a Unity-based simulation program developed using the Unity ML-Agents toolkit. Our goal is to reach the target position with high accuracy, so here we can use the state-of-the-art Deep Deterministic Policy Gradient (DDPG) algorithm designed for continuous state and action spaces.

Real World Applications

Robotic arms play a critical role in manufacturing, production facilities, space exploration and search and rescue operations. It is very important to control the robot arm with high precision and flexibility. By employing reinforcement learning techniques, these robotic systems can be enabled to learn and adjust their behavior in real time, thereby improving performance and flexibility. Advances in reinforcement learning not only contribute to our understanding of artificial intelligence, but have the potential to revolutionize industries and have a meaningful impact on society.

Reacher is a robotic arm simulator that is often used for the development and testing of control algorithms. It provides a virtual environment that simulates the physical characteristics and motion laws of the robotic arm, allowing developers to conduct research and experiments on control algorithms without the need for actual hardware.

Reacher's environment mainly consists of the following parts:

1. Robotic arm: Reacher simulates a double-jointed robotic arm, including a fixed base and two movable joints. Developers can change the attitude and position of the robotic arm by controlling its two joints.
2. Target point: Within the movement range of the robotic arm, Reacher provides a target point, and the position of the target point is randomly generated. The developer's task is to control the robotic arm so that the end of the robotic arm can contact the target point.
3. Physics engine: Reacher uses a physics engine to simulate the physical characteristics and movement patterns of the robotic arm. Developers can simulate different physical environments by adjusting the parameters of the physics engine.
4. Visual interface: Reacher provides a visual interface that can display the positions of the robotic arm and target points, as well as the posture and movement trajectory of the robotic arm. Developers can debug and optimize control algorithms through a visual interface.

Reacher simulator is a very practical tool that can help developers quickly test and optimize control algorithms without the need for actual hardware.

Simulation Environment

Reacher is built using the Unity ML-Agents toolkit, our agent can control a dual-jointed robotic arm. The goal is to guide the arm toward the target position and maintain its position within the target area for as long as possible. The environment features 20 synchronized agents, each running independently, which helps to efficiently collect experience during training.

State and Action Space

Understanding state and action space is crucial to designing effective reinforcement learning algorithms. In the Reacher environment, the state space consists of 33 continuous variables that provide information about the robotic arm, such as its position, rotation, velocity, and angular velocity. The action space is also continuous, with four variables corresponding to the torques exerted on the two joints of the robotic arm. Each action variable is a real number between -1 and 1.

Task Types and Success Criteria

Reacher tasks are considered to be fragmented, with each fragment containing a fixed number of time steps. The agent's goal is to maximize its total reward during these steps. The arm end effector receives a 0.1 bonus for each step it takes to maintain the target position. Success is considered when an agent achieves an average score of 30 points or above over 100 consecutive operations.

Now that we understand the environment, let's explore the DDPG algorithm, its implementation, and how it effectively solves continuous control problems in this environment.

Algorithm Selection for Continuous Control: DDPG

When it comes to continuous control tasks like the Reacher problem, algorithm selection is critical to achieving optimal performance. In this project, we chose the DDPG algorithm because it is an actor-critic method specifically designed to handle continuous state and action spaces.

The DDPG algorithm combines the advantages of policy-based and value-based methods by combining two neural networks: the actor network determines the best behavior given the current state, and the critic network network) estimates the state-behavior value function (Q-function). Both types of networks have target networks that stabilize the learning process by providing a fixed target during the update process.

By using the Critic network to estimate the q function and the Actor network to determine the optimal behavior, the DDPG algorithm effectively combines the advantages of the policy gradient method and DQN. This hybrid approach allows agents to learn efficiently in a continuous control environment.

<code>import random from collections import deque import torch import torch.nn as nn import numpy as np  from actor_critic import Actor, Critic  class ReplayBuffer: def __init__(self, buffer_size, batch_size): self.memory = deque(maxlen=buffer_size) self.batch_size = batch_size  def add(self, state, action, reward, next_state, done): self.memory.append((state, action, reward, next_state, done))  def sample(self): batch = random.sample(self.memory, self.batch_size) states, actions, rewards, next_states, dones = zip(*batch) return states, actions, rewards, next_states, dones  def __len__(self): return len(self.memory)   class DDPG: def __init__(self, state_dim, action_dim, hidden_dim, buffer_size, batch_size, actor_lr, critic_lr, tau, gamma): self.actor = Actor(state_dim, hidden_dim, action_dim, actor_lr) self.actor_target = Actor(state_dim, hidden_dim, action_dim, actor_lr) self.critic = Critic(state_dim, action_dim, hidden_dim, critic_lr) self.critic_target = Critic(state_dim, action_dim, hidden_dim, critic_lr)  self.memory = ReplayBuffer(buffer_size, batch_size) self.batch_size = batch_size self.tau = tau self.gamma = gamma  self._update_target_networks(tau=1)# initialize target networks  def act(self, state, noise=0.0): state = torch.tensor(state, dtype=torch.float32).unsqueeze(0) action = self.actor(state).detach().numpy()[0] return np.clip(action + noise, -1, 1)  def store_transition(self, state, action, reward, next_state, done): self.memory.add(state, action, reward, next_state, done)  def learn(self): if len(self.memory) </code>

The above code also uses Replay Buffer, which can improve learning efficiency and stability. Replay Buffer is essentially a memory data structure that stores a fixed number of past experiences or transitions, consisting of status, action, reward, next status and completion information. The main advantage of using it is to enable the agent to break correlations between consecutive experiences, thereby reducing the impact of harmful temporal correlations.

By drawing random mini-batches of experience from the buffer, the agent can learn from a diverse set of transformations, which helps stabilize and generalize the learning process. Replay Buffers also allow agents to reuse past experiences multiple times, thereby increasing data efficiency and promoting more effective learning from limited interactions with the environment.

The DDPG algorithm is a good choice because of its ability to efficiently handle continuous action spaces, which is a key aspect in this environment. The design of the algorithm allows efficient utilization of parallel experience gathered by multiple agents, resulting in faster learning and better convergence. Just like the Reacher introduced above, it can run 20 agents at the same time, so we can use these 20 agents to share experience, learn collectively, and increase the learning speed.

After completing the algorithm, we will introduce the hyperparameter selection and training process below.

DDPG algorithm works in the Reacher environment

To better understand the effectiveness of the algorithm in the environment, we need to take a closer look at the key components and steps involved in the learning process.

Network Architecture

The DDPG algorithm uses two neural networks, Actor and Critic. Both networks contain two hidden layers, each containing 400 nodes. The hidden layer uses the ReLU (Rectified Linear Unit) activation function, while the output layer of the Actor network uses the tanh activation function to generate actions ranging from -1 to 1. The output layer of the critic network has no activation function because it directly estimates the q function.

The following is the code of the network:

<code>import numpy as np import torch import torch.nn as nn import torch.optim as optim  class Actor(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, learning_rate=1e-4): super(Actor, self).__init__()  self.fc1 = nn.Linear(input_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim, hidden_dim) self.fc3 = nn.Linear(hidden_dim, output_dim)  self.tanh = nn.Tanh()  self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)  def forward(self, state): x = torch.relu(self.fc1(state)) x = torch.relu(self.fc2(x)) x = self.tanh(self.fc3(x)) return x  class Critic(nn.Module): def __init__(self, state_dim, action_dim, hidden_dim, learning_rate=1e-4): super(Critic, self).__init__()  self.fc1 = nn.Linear(state_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim + action_dim, hidden_dim) self.fc3 = nn.Linear(hidden_dim, 1)  self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)  def forward(self, state, action): x = torch.relu(self.fc1(state)) x = torch.relu(self.fc2(torch.cat([x, action], dim=1))) x = self.fc3(x) return x</code>

Hyperparameter selection

The selected hyperparameters are crucial for efficient learning. In this project, our Replay Buffer size is 200,000 and the batch size is 256. The learning rate of Actor is 5e-4, the learning rate of Critic is 1e-3, the soft update parameter (tau) is 5e-3, and gamma is 0.995. Finally, action noise was added, with an initial noise scale of 0.5 and a noise attenuation rate of 0.998.

Training process

The training process involves continuous interaction between the two networks, and with 20 parallel agents sharing the same network, the model learns collectively from the experience collected by all agents. This setup speeds up the learning process and increases efficiency.

<code>from collections import deque import numpy as np import torch  from ddpg import DDPG  def train_ddpg(env, agent, episodes, max_steps, num_agents, noise_scale=0.1, noise_decay=0.99): scores_window = deque(maxlen=100) scores = []  for episode in range(1, episodes + 1): env_info = env.reset(train_mode=True)[brain_name] states = env_info.vector_observations agent_scores = np.zeros(num_agents)  for step in range(max_steps): actions = agent.act(states, noise_scale) env_info = env.step(actions)[brain_name] next_states = env_info.vector_observations rewards = env_info.rewards dones = env_info.local_done  for i in range(num_agents): agent.store_transition(states[i], actions[i], rewards[i], next_states[i], dones[i]) agent.learn()  states = next_states agent_scores += rewards noise_scale *= noise_decay  if np.any(dones): break  avg_score = np.mean(agent_scores) scores_window.append(avg_score) scores.append(avg_score)  if episode % 10 == 0: print(f"Episode: {episode}, Score: {avg_score:.2f}, Avg Score: {np.mean(scores_window):.2f}")  # Saving trained Networks torch.save(agent.actor.state_dict(), "actor_final.pth") torch.save(agent.critic.state_dict(), "critic_final.pth")  return scores  if __name__ == "__main__": env = UnityEnvironment(file_name='Reacher_20.app') brain_name = env.brain_names[0] brain = env.brains[brain_name]  state_dim = 33 action_dim = brain.vector_action_space_size  num_agents = 20  # Hyperparameter suggestions hidden_dim = 400 batch_size = 256 actor_lr = 5e-4 critic_lr = 1e-3 tau = 5e-3 gamma = 0.995 noise_scale = 0.5 noise_decay = 0.998  agent = DDPG(state_dim, action_dim, hidden_dim=hidden_dim, buffer_size=200000, batch_size=batch_size,actor_lr=actor_lr, critic_lr=critic_lr, tau=tau, gamma=gamma)  episodes = 200 max_steps = 1000  scores = train_ddpg(env, agent, episodes, max_steps, num_agents, noise_scale=0.2, noise_decay=0.995)</code>

The key steps in the training process are as follows:

Initialize the network: The agent initializes the shared Actor and Critic networks and their respective target networks with random weights. The target network provides stable learning targets during updates.

• Interacting with the environment: Each agent uses a shared Actor network to interact with the environment by selecting actions based on its current state. To encourage exploration, a noise term is also added to the actions in the initial stages of training. After taking an action, each agent observes the resulting reward and next state.
• Storing experience: Each agent stores the observed experience (state, action, reward, next_state) in the shared replay buffer. This buffer contains a fixed amount of recent experience so that each agent can learn from various transitions collected by all agents.
• Learn from experience: Periodically extract a batch of experiences from the shared replay buffer. Use sampling experience to update the shared critic network by minimizing the mean square error between the predicted Q-value and the target Q-value.
• Update Actor Network: The shared Actor network is updated using the policy gradient, which is calculated by taking the output gradient of the shared Critic network with respect to the selected action. The shared actor network learns to choose actions that maximize the expected Q-value.
• Update target network: The shared Actor and Critic target networks are soft updated using a mixture of current and target network weights. This ensures a stable learning process.

Result Display

Our agent successfully learned to control a double-jointed robotic arm in the Racher environment using the DDPG algorithm. Throughout the training process, we monitor the agent's performance based on the average score of all 20 agents. As the agent explores the environment and gathers experience, its ability to predict optimal behavior for reward maximization improves significantly.

It can be seen that the agent showed significant proficiency in the task, with the average score exceeding the threshold required to solve the environment (30), although the agent's performance varied throughout There are differences during the training process, but the overall trend is upward, indicating that the learning process is successful.

The graph below shows the average score of 20 agents:

#You can see that the DDPG algorithm we implemented effectively solved the problem of the Racher environment. Agents are able to adjust their behavior and achieve expected performance in tasks.

Next steps

The hyperparameters in this project were selected based on a combination of recommendations from the literature and empirical testing. Further optimization through system hyperparameter tuning may lead to better performance.

Multi-agent parallel training: In this project, we use 20 agents to collect experience at the same time. The impact of using more agents on the overall learning process may result in faster convergence or improved performance.

Batch normalization: To further enhance the learning process, implementing batch normalization in neural network architectures is worth exploring. By normalizing the input features of each layer during training, batch normalization can help reduce internal covariate shifts, speed up learning, and potentially improve generalization. Adding batch normalization to the Actor and Critic networks may lead to more stable and efficient training, but this requires further testing.

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