#Reinforcement Learning
#Reinforcement Learning
#Reinforcement Learning
#Reinforcement Learning
#Reinforcement Learning
#Reinforcement Learning
#OpenAI Gym
#OpenAI Gym
#OpenAI Gym
#OpenAI Gym
#OpenAI Gym
#OpenAI Gym
#Stable-Baselines3
#Stable-Baselines3
#Stable-Baselines3
#Stable-Baselines3
#Stable-Baselines3
#Stable-Baselines3
Break Out
Break Out
Break Out
Break Out
Break Out
Break Out
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
One of the famous Atari games. You move a paddle and hit the ball in a brick wall at the top of the screen. Your goal is to destroy the brick wall. You can try to break through the wall and let the ball wreak havoc on the other side, all on its own!
It's fun playing trying to hit all the bricks, but curious to witness flawless gameplay where every move is calculated to perfection? That's what I did below. Watch as our trained agent effortlessly conquers every brick, achieving the ultimate high score.
2023 - present
Timeline
2023 - present
Timeline
Background
Background
The goal of the game is to clear out all the bricks by hitting them with a ball. The agent can only control the paddle located at the bottom of the game.
The actions/action space of the agent is [0: No operation, 1: Fire, 2: Right, 3: Left]
The game is observed in a GUI environment that returns a RGB image displayed to humans.
The rewards given to the agent are on the basis of the colour of the brick that's been destroyed. For detailed reward information, see Atari Page.
The goal of the game is to clear out all the bricks by hitting them with a ball. The agent can only control the paddle located at the bottom of the game.
The actions/action space of the agent is [0: No operation, 1: Fire, 2: Right, 3: Left]
The game is observed in a GUI environment that returns a RGB image displayed to humans.
The rewards given to the agent are on the basis of the colour of the brick that's been destroyed. For detailed reward information, see Atari Page.
Training
Training
1
Algorithm: A2C (Advantage Actor-Critic)
A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.
1
Algorithm: A2C (Advantage Actor-Critic)
A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.
1
Algorithm: A2C (Advantage Actor-Critic)
A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.
1
Algorithm: A2C (Advantage Actor-Critic)
A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.
1
Algorithm: A2C (Advantage Actor-Critic)
A2C (Advantage Actor-Critic) is a specific variant of the Actor-Critic algorithm that introduces the concept of the advantage function. This function measures how much better an action is compared to the average action in a given state. By incorporating this advantage information, A2C focuses the learning process on actions that have a significantly higher value than the typical action taken in that state.
2
Training Method: Vectorized environment
In a vectorized environment, multiple parallel environments are run simultaneously to speed up training, especially for algorithms like deep reinforcement learning that can benefit from parallelism. The agent is trained under a class of vectorized environment called VecFrameStack. VecFrameStack specifically stacks consecutive observations from these parallel environments to create a single observation, effectively converting a batch of observations into a single observation that includes information from multiple time steps.
2
Training Method: Vectorized environment
In a vectorized environment, multiple parallel environments are run simultaneously to speed up training, especially for algorithms like deep reinforcement learning that can benefit from parallelism. The agent is trained under a class of vectorized environment called VecFrameStack. VecFrameStack specifically stacks consecutive observations from these parallel environments to create a single observation, effectively converting a batch of observations into a single observation that includes information from multiple time steps.
2
Training Method: Vectorized environment
In a vectorized environment, multiple parallel environments are run simultaneously to speed up training, especially for algorithms like deep reinforcement learning that can benefit from parallelism. The agent is trained under a class of vectorized environment called VecFrameStack. VecFrameStack specifically stacks consecutive observations from these parallel environments to create a single observation, effectively converting a batch of observations into a single observation that includes information from multiple time steps.
2
Training Method: Vectorized environment
In a vectorized environment, multiple parallel environments are run simultaneously to speed up training, especially for algorithms like deep reinforcement learning that can benefit from parallelism. The agent is trained under a class of vectorized environment called VecFrameStack. VecFrameStack specifically stacks consecutive observations from these parallel environments to create a single observation, effectively converting a batch of observations into a single observation that includes information from multiple time steps.
2
Training Method: Vectorized environment
In a vectorized environment, multiple parallel environments are run simultaneously to speed up training, especially for algorithms like deep reinforcement learning that can benefit from parallelism. The agent is trained under a class of vectorized environment called VecFrameStack. VecFrameStack specifically stacks consecutive observations from these parallel environments to create a single observation, effectively converting a batch of observations into a single observation that includes information from multiple time steps.
3
Timesteps
The agent is trained on an average timesteps of 1,00,000 which resulted in optimal performance. After training and evaluation the agent is able to play and achieve highscores.
3
Timesteps
The agent is trained on an average timesteps of 1,00,000 which resulted in optimal performance. After training and evaluation the agent is able to play and achieve highscores.
3
Timesteps
The agent is trained on an average timesteps of 1,00,000 which resulted in optimal performance. After training and evaluation the agent is able to play and achieve highscores.
3
Timesteps
The agent is trained on an average timesteps of 1,00,000 which resulted in optimal performance. After training and evaluation the agent is able to play and achieve highscores.
3
Timesteps
The agent is trained on an average timesteps of 1,00,000 which resulted in optimal performance. After training and evaluation the agent is able to play and achieve highscores.
Libraries
Libraries
1
OpenAI Gym
OpenAI Gym is a Pythonic API that provides simulated training environments to train and test reinforcement learning agents. It's become the industry standard API for reinforcement learning and is essentially a toolkit for training RL algorithms.
1
OpenAI Gym
OpenAI Gym is a Pythonic API that provides simulated training environments to train and test reinforcement learning agents. It's become the industry standard API for reinforcement learning and is essentially a toolkit for training RL algorithms.
1
OpenAI Gym
OpenAI Gym is a Pythonic API that provides simulated training environments to train and test reinforcement learning agents. It's become the industry standard API for reinforcement learning and is essentially a toolkit for training RL algorithms.
1
OpenAI Gym
OpenAI Gym is a Pythonic API that provides simulated training environments to train and test reinforcement learning agents. It's become the industry standard API for reinforcement learning and is essentially a toolkit for training RL algorithms.
1
OpenAI Gym
OpenAI Gym is a Pythonic API that provides simulated training environments to train and test reinforcement learning agents. It's become the industry standard API for reinforcement learning and is essentially a toolkit for training RL algorithms.
2
Stable-Baselines3
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. Stable baselines provides features like Unified structure for all algorithms, clean code and tensorboard support.
2
Stable-Baselines3
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. Stable baselines provides features like Unified structure for all algorithms, clean code and tensorboard support.
2
Stable-Baselines3
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. Stable baselines provides features like Unified structure for all algorithms, clean code and tensorboard support.
2
Stable-Baselines3
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. Stable baselines provides features like Unified structure for all algorithms, clean code and tensorboard support.
2
Stable-Baselines3
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. Stable baselines provides features like Unified structure for all algorithms, clean code and tensorboard support.
3
Make_atari_env
This function facilitates the setup of Atari environments by automatically configuring the environment according to the specifications required for reinforcement learning tasks, such as frame stacking, grayscale observation, and frame skipping. It simplifies the process of setting up Atari game environments for training reinforcement learning agents.
3
Make_atari_env
This function facilitates the setup of Atari environments by automatically configuring the environment according to the specifications required for reinforcement learning tasks, such as frame stacking, grayscale observation, and frame skipping. It simplifies the process of setting up Atari game environments for training reinforcement learning agents.
3
Make_atari_env
This function facilitates the setup of Atari environments by automatically configuring the environment according to the specifications required for reinforcement learning tasks, such as frame stacking, grayscale observation, and frame skipping. It simplifies the process of setting up Atari game environments for training reinforcement learning agents.
3
Make_atari_env
This function facilitates the setup of Atari environments by automatically configuring the environment according to the specifications required for reinforcement learning tasks, such as frame stacking, grayscale observation, and frame skipping. It simplifies the process of setting up Atari game environments for training reinforcement learning agents.
3
Make_atari_env
This function facilitates the setup of Atari environments by automatically configuring the environment according to the specifications required for reinforcement learning tasks, such as frame stacking, grayscale observation, and frame skipping. It simplifies the process of setting up Atari game environments for training reinforcement learning agents.
Tools
Tools
Jupyter Notebook
OpenAI Gym
Conclusion
In conclusion, this project has successfully demonstrated the power of reinforcement learning in training agents to plays Atari games. Through training and optimisation, we have witnessed the agent evolve from a novice player to a skilled contender, capable of achieving remarkable scores.