Mastering CartPole-v1 with QLearning

Table of Contents

1. Introduction
2. Carpool Environment
3. Vanilla DQN Agent
4. Double Deep Q Learning
5. Experience Replay
6. Prioritized Replay
7. Dueling Architecture
8. Experiments and Results
9. Benchmark Test
10. Future Work

Introduction

In this article, we will be reviewing the approach of solving carpool using deep reinforcement learning. We will start by discussing the carpool environment and its components. Then, we will Delve into different implementations, results, and future work. Let's dive in and explore the fascinating world of using deep reinforcement learning to solve carpool problems.

Carpool Environment

The carpool environment is designed around the concept of an inverted pendulum, represented by a pole with a center of gravity over its pivot point. It moves along a Frictionless track and is controlled by applying a force of +1 or -1 to the cart. The goal of the carpool agent is to prevent the pole from falling over.

The state space of the carpool environment consists of four values - cart position, cart velocity, pole angle, and the velocity of the tip of the pole. The action space consists of two actions - moving left or moving right. A reward of +1 is provided for every time step where the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical or the cart moves more than 2.4 units from the center.

Vanilla DQN Agent

The first implementation we will discuss is the Vanilla DQN agent. This agent consists of an input layer of size 4, which corresponds to the size of the state space, and three dense layers of sizes 24, 24, and 12. The output of the agent provides the Q-values for each action. The model optimization is performed using the Adam optimizer along with the Huber loss metric.

However, the Vanilla DQN agent has proven to be inefficient and unable to achieve sufficient rewards. Therefore, further improvements have been implemented.

Double Deep Q Learning

The Double Deep Q Learning model aims to reduce the substantial overestimation of action values that can occur in DQN models. This approach generally leads to better performance. Double Q Learning achieves this by decomposing the operation of choosing the best action in the target into action selection and action evaluation. The target network evaluates the quality of the chosen action, while the Q network determines the chosen action Based on the evaluation.

Experience replay is another technique that has been known to improve the learning process of deep reinforcement learning agents. It reduces the amount of experience the agent needs to learn by allowing it to remember and reuse its previous experiences. This involves stochastically sampling from a replay buffer, resulting in lower computational costs compared to agents that only Interact with the environment.

Prioritized Replay

Prioritized replay is a modification of experience replay that aims to prioritize the sampling of transitions based on their importance. Transitions with higher temporal difference errors, which indicate more important transitions, are sampled more frequently. This approach results in more effective and efficient sampling, as the agent's competence increases with the training. Transitions that become less surprising and more task-Relevant are prioritized, reducing the need for computationally expensive transition processing.

Dueling Architecture

The dueling architecture is a neural network model design that explicitly separates the representation of state values and state-dependent action advantages into two separate streams. This allows the model to learn which states are valuable without having to learn the effect of each action for each state. The training process of the dueling model is identical to the standard DQN architecture, as they share the same input/output interface.

Experiments and Results

In this section, we will discuss the experiments conducted and the results obtained from testing the different models.

• Vanilla DQN Agent: The agent showed wide and long variation in rewards, indicating high exploration. It performed the most exploration but had lower average rewards.
• Double Q Learning: The agent showed compact variation and a steeper average learning curve, indicating better performance than the Vanilla DQN agent.
• Prioritized Replay: Training the agents with prioritized replay resulted in a more consistent learning rate and lower variation in rewards. It demonstrated faster learning than the other models.

Benchmark Test

A benchmark test was conducted to compare the carpool agent's performance against the OpenAI Gym threshold. The threshold required the agent to receive at least a reward of 195 for 100 consecutive episodes. The agent trained with a combination of methods, including prioritized replay, showed the most consistent learning and achieved significantly higher rewards.

The combination model was further tested with 1 million frames and reached rewards of over 500 initially. However, it experienced a steep drop in rewards, indicating possible overfitting. This is a known issue in continuous environments for reinforcement learning models.

Future Work

In our Journey to solve carpool using deep reinforcement learning, there are a few exciting avenues for future work:

1. Rainbow Agent: The Rainbow agent, introduced by DeepMind in 2018, combines multiple advanced techniques in deep reinforcement learning. Implementing this agent could further enhance the performance of our models.
2. Agent 57: DeepMind's Agent 57, introduced in 2020, outperforms the standard human benchmark in all 57 Atari games. It incorporates simple changes, such as dynamically adjusting the discount factor and increasing the backpropagation through time window. Exploring the implementation of Agent 57 could unlock new possibilities for solving carpool problems.

With further research and advancements in deep reinforcement learning, we can Continue to improve the performance and efficiency of carpool agents.

In conclusion, our approach to solving carpool using deep reinforcement learning has shown promising results. By implementing advanced techniques such as double deep Q learning, prioritized replay, and the dueling architecture, we were able to enhance the learning process and improve the performance of the agents. The future holds exciting possibilities for further advancements in this field, and We Are confident that deep reinforcement learning is a powerful tool for solving complex problems like carpool. Thank You for joining us on this journey.

Highlights:

• Carpool environment with an inverted pendulum
• Vanilla DQN agent implementation and limitations
• Double deep Q learning to reduce overestimation of action values
• Experience replay and prioritized replay to improve the learning process
• Dueling architecture to separate state values and action advantages
• Experiments and results of different models
• Benchmark test against OpenAI Gym threshold
• Future work with Rainbow Agent and Agent 57

FAQ:

Q: What is the carpool environment? A: The carpool environment consists of an inverted pendulum represented by a pole, where the goal is to prevent it from falling over while moving along a frictionless track.

Q: What is the Vanilla DQN agent? A: The Vanilla DQN agent is an implementation of deep Q learning with a specific neural network architecture. However, it has limitations in terms of achieving high rewards.

Q: How does double deep Q learning work? A: Double deep Q learning decomposes the operation of choosing the best action into action selection and action evaluation, reducing overestimation of action values and improving performance.

Q: What is prioritized replay? A: Prioritized replay is a modification of experience replay that prioritizes the sampling of transitions based on their importance, improving the efficiency and effectiveness of learning.

Q: What is the dueling architecture? A: The dueling architecture is a neural network model design that separates the representation of state values and state-dependent action advantages, allowing the model to learn valuable states without explicitly learning each action's effect.

Q: What were the results of the experiments? A: The experiments showed that the Vanilla DQN agent had wide variation, while the double Q learning agent had compact variation and a steeper learning curve. Prioritized replay resulted in more consistent learning rates and lower variation.

Q: How did the agent perform in the benchmark test? A: The agent trained with a combination of methods, including prioritized replay, showed the most consistent learning and achieved higher rewards compared to the OpenAI Gym threshold.

Q: What are some future directions for this research? A: Future work includes implementing advanced agents like the Rainbow agent and Agent 57, which have shown improvements in other domains. These agents could further enhance the performance of carpool solving using deep reinforcement learning.