Deep Q-learning reinforcement learning using Panda-Gym's robotic arm simulation

Reinforcement learning (RL) is a machine learning method that allows an agent to learn how to behave in its environment through trial and error. Agents are rewarded or punished for taking actions that lead to desired outcomes. Over time, the agent learns to take actions that maximize its expected reward

RL agents typically use a Markov decision process ( MDP), a mathematical framework for modeling sequential decision problems. MDP consists of four parts:

• #State: A set of possible states of the environment.
• Action: A set of actions that an agent can take.
• Transition function: A function that predicts the probability of transitioning to a new state given the current state and action.
• Reward function: A function that assigns a reward to the agent for each conversion.

The goal of the agent is to learn a policy function that maps states to actions. Maximize the agent's expected return over time through a policy function.

Deep Q-learning is a reinforcement learning algorithm that uses deep neural networks to learn policy functions. Deep neural networks take the current state as input and output a vector of values, where each value represents a possible action. The agent then takes the action based on the highest value

Deep Q-learning is a value-based reinforcement learning algorithm, which means it learns the value of each state-action pair. The value of a state-action pair is the expected reward for the agent to take that action in that state.

Actor-Critic is a RL algorithm that combines value-based and policy-based. There are two components:

#Actor: The actor is responsible for selecting operations.

Critic: Responsible for evaluating the Actor's behavior.

Actors and critics are trained at the same time. Actors are trained to maximize expected rewards and critics are trained to accurately predict expected rewards for each state-action pair

The Actor-Critic algorithm has several advantages over other reinforcement learning algorithms advantage. First, it is more stable, which means that bias is less likely to occur during training. Second, it's more efficient, which means it can learn faster. Third, it has better scalability and can be applied to problems with large state and operation spaces

The table below summarizes the differences between Deep Q-learning and Actor-Critic The main differences:

Advantages of Actor-Critic (A2C)

Actor-Critic is A popular reinforcement learning architecture that combines policy-based and value-based approaches. It has many advantages that make it a strong choice for solving various reinforcement learning tasks:

1. Low variance

Compared Compared to traditional policy gradient methods, A2C usually has lower variance during training. This is because A2C uses both the policy gradient and the value function, and uses the value function to reduce variance in the calculation of the gradient. Low variance means that the training process is more stable and can converge to a better strategy faster

2. Faster learning speed

Due to Due to the low variance feature, A2C can usually learn a good strategy faster. This is especially important for tasks that require extensive simulations, as faster learning speeds save valuable time and computing resources.

3. Combining policy and value function

One of the distinctive features of A2C is that it learns policy and value function simultaneously. This combination enables the agent to better understand the correlation between environment and actions, thereby better guiding policy improvements. The existence of the value function also helps reduce errors in policy optimization and improve training efficiency.

4. Supports continuous and discrete action spaces

A2C can adapt to different types of action spaces, including continuous and discrete actions, and is very versatile . This makes A2C a widely applicable reinforcement learning algorithm that can be applied to a variety of tasks, from robot control to gameplay optimization

5. Parallel training

A2C can be easily parallelized to take full advantage of multi-core processors and distributed computing resources. This means more empirical data can be collected in less time, thus improving training efficiency.

Although Actor-Critic methods have some advantages, they also face some challenges, such as hyperparameter tuning and potential instability in training. However, with appropriate tuning and techniques such as experience replay and target networks, these challenges can be mitigated to a large extent, making Actor-Critic a valuable approach in reinforcement learning

panda-gym

##panda-gym is developed based on the PyBullet engine and encapsulates reach, push, slide, Six tasks including pick&place, stack, and flip are mainly inspired by OpenAI Fetch.

We will use panda-gym as an example to show the following code

1. Installation library

First, we need to initialize the code of the reinforcement learning environment:

!apt-get install -y \libgl1-mesa-dev \libgl1-mesa-glx \libglew-dev \xvfb \libosmesa6-dev \software-properties-common \patchelf  !pip install \free-mujoco-py \pytorch-lightning \optuna \pyvirtualdisplay \PyOpenGL \PyOpenGL-accelerate\stable-baselines3[extra] \gymnasium \huggingface_sb3 \huggingface_hub \ panda_gym

2. Import library

import os  import gymnasium as gym import panda_gym  from huggingface_sb3 import load_from_hub, package_to_hub  from stable_baselines3 import A2C from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize from stable_baselines3.common.env_util import make_vec_env

3. Create a running environment

env_id = "PandaReachDense-v3"  # Create the env env = gym.make(env_id)  # Get the state space and action space s_size = env.observation_space.shape a_size = env.action_space  print("\n _____ACTION SPACE_____ \n") print("The Action Space is: ", a_size) print("Action Space Sample", env.action_space.sample()) # Take a random action

4. Standardization of observations and rewards

A good way to optimize reinforcement learning is to optimize the input Features are normalized. We calculate the running mean and standard deviation of the input features through the wrapper. At the same time, the reward is normalized by adding norm_reward = True

env = make_vec_env(env_id, n_envs=4)  env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.)

5, creating an A2C model

We use the official model trained by the Stable-Baselines3 team Agent

model = A2C(policy = "MultiInputPolicy",env = env,verbose=1)

6, Training A2C

model.learn(1_000_000)  # Save the model and VecNormalize statistics when saving the agent model.save("a2c-PandaReachDense-v3") env.save("vec_normalize.pkl")

7, Evaluating agent

from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize  # Load the saved statistics eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")]) eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)  # We need to override the render_mode eval_env.render_mode = "rgb_array"  # do not update them at test time eval_env.training = False # reward normalization is not needed at test time eval_env.norm_reward = False  # Load the agent model = A2C.load("a2c-PandaReachDense-v3")  mean_reward, std_reward = evaluate_policy(model, eval_env)  print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

Summary

In "panda-gym", the effective combination of the Panda robotic arm and the GYM environment allows us to easily perform reinforcement learning of the robotic arm locally,

In the Actor-Critic architecture, the agent learns to make incremental improvements at each time step, which is in contrast to the sparse reward function (in which the result is binary), which makes the Actor-Critic method Particularly suitable for such tasks.

By seamlessly combining policy learning and value estimation, the robot agent is able to skillfully manipulate the robotic arm end effector to accurately reach the designated target position. This not only provides practical solutions for tasks such as robot control, but also has the potential to transform a variety of fields that require agile and informed decision-making

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