diff --git a/.gitignore b/.gitignore
index 03f7fb4..8f53b49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -161,5 +161,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
 
-src/mnist/data/MNIST
-src/mnist/models/*
+src/classification/data/MNIST
diff --git a/poetry.lock b/poetry.lock
index fef837b..b6f2aeb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -75,15 +75,29 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
+name = "box2d-py"
+version = "2.3.5"
+description = "Python Box2D"
+optional = false
+python-versions = "*"
+files = [
+    {file = "box2d-py-2.3.5.tar.gz", hash = "sha256:b37dc38844bcd7def48a97111d2b082e4f81cca3cece7460feb3eacda0da2207"},
+    {file = "box2d_py-2.3.5-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:287aa54005c0644b47bf7ad72966e4068d66e56bcf8458f5b4a653ffe42a2618"},
+    {file = "box2d_py-2.3.5-cp36-cp36m-macosx_10_13_x86_64.whl", hash = "sha256:483b3f9acd5d156b72bf2013f93cf7f8ca0ee1562e43d2353ab4c0cbec4ee49a"},
+    {file = "box2d_py-2.3.5-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:a294c2d7cc73cc05dd491287079e15419eb98caa3158df94f40faf85eeb4b6e9"},
+    {file = "box2d_py-2.3.5-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:0d46068eb8d29e366ed698ab2a4833d4d2d34ed035ebd6a685888007dda05f64"},
+]
+
 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -199,6 +213,17 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cloudpickle"
+version = "3.0.0"
+description = "Pickler class to extend the standard pickle.Pickler functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
+    {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -323,6 +348,17 @@ files = [
 dnspython = ">=2.0.0"
 idna = ">=2.0.0"
 
+[[package]]
+name = "farama-notifications"
+version = "0.0.4"
+description = "Notifications for all Farama Foundation maintained libraries."
+optional = false
+python-versions = "*"
+files = [
+    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
+    {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
+]
+
 [[package]]
 name = "fastapi"
 version = "0.111.0"
@@ -385,53 +421,53 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
-version = "4.53.0"
+version = "4.53.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -450,13 +486,13 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
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+version = "2024.6.1"
 description = "File-system specification"
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 python-versions = ">=3.8"
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 tqdm = ["tqdm"]
 
+[[package]]
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+description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
+optional = false
+python-versions = ">=3.8"
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+
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+toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
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 [[package]]
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 version = "0.14.0"
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 [package.dependencies]
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 [[package]]
@@ -1166,57 +1235,62 @@ files = [
 
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 mic = ["olefile"]
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+    {file = "pygame-2.6.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e909186d4d512add39b662904f0f79b73028fbfc4fbfdaf6f9412aed4e500e9c"},
+    {file = "pygame-2.6.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:79abcbf6d12fce51a955a0652ccd50b6d0a355baa27799535eaf21efb43433dd"},
+    {file = "pygame-2.6.0.tar.gz", hash = "sha256:722d33ae676aa8533c1f955eded966411298831346b8d51a77dad22e46ba3e35"},
+]
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -1707,19 +1871,47 @@ anyio = ">=3.4.0,<5"
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
+[[package]]
+name = "swig"
+version = "4.2.1"
+description = "SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages."
+optional = false
+python-versions = "*"
+files = [
+    {file = "swig-4.2.1-py2.py3-none-macosx_10_9_universal2.whl", hash = "sha256:3b7389a1c86d39637b7364f11a1dd8cfb52b1ebba40a2356d2ded74498f4cff7"},
+    {file = "swig-4.2.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:5c8826b415d6feaca2bbf1f4bd0ff900d6dbb9ddd3b1850f95129b8aac1eb44b"},
+    {file = "swig-4.2.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:6f30b409d20b1e96f5432bf94e0106cf68596884b6da70323d5a21b8f43e3f3b"},
+    {file = "swig-4.2.1-py2.py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c4d8e3c928d5efba698b4610a9292b51a0597958ced79506f76f102ac621184"},
+    {file = "swig-4.2.1-py2.py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4f8931d0f33a91a611f084a26ecf1364845b5ff8b417db2b99cc8b8a4d8215e3"},
+    {file = "swig-4.2.1-py2.py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9f3482aadf1d6ce3122a84fcd854c32dd5031dd65dba5d5dd9f73deb7333c505"},
+    {file = "swig-4.2.1-py2.py3-none-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7ce1fd4518ca0a63b809177ed8e0ec11ab485f8ebb66752605c978b964e75b5b"},
+    {file = "swig-4.2.1-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:c7b2db2ccff556dbfc88ef98773b3524b18bc4077ea1d7a5d7339c3a1f3f70dc"},
+    {file = "swig-4.2.1-py2.py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:0cd1812ef95a806eec82ee74b601aa0afd616beffb4227afe090bdc12f690286"},
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+    {file = "swig-4.2.1-py2.py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:30bfb7d8f4bc6ae843c9f36825abfe1fbdadf0982cbb119af64da3f750c01620"},
+    {file = "swig-4.2.1-py2.py3-none-win32.whl", hash = "sha256:41cbe797f6ba49fc600c629928e1afd2632be23bc7d1603543360234e1773dae"},
+    {file = "swig-4.2.1-py2.py3-none-win_amd64.whl", hash = "sha256:2482156f6fba8a8a720246a95d203530834e3d8da981f904ef71c1a7e9d68efe"},
+    {file = "swig-4.2.1.tar.gz", hash = "sha256:cdbb1748132ce99defaf4ebf1736c457fb13788183264af28f17d0d477e109ba"},
+]
+
 [[package]]
 name = "sympy"
-version = "1.12.1"
+version = "1.13.0"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
-    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
+    {file = "sympy-1.13.0-py3-none-any.whl", hash = "sha256:6b0b32a4673fb91bd3cac3b55406c8e01d53ae22780be467301cc452f6680c92"},
+    {file = "sympy-1.13.0.tar.gz", hash = "sha256:3b6af8f4d008b9a1a6a4268b335b984b23835f26d1d60b0526ebc71d48a25f57"},
 ]
 
 [package.dependencies]
-mpmath = ">=1.1.0,<1.4.0"
+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
 [[package]]
 name = "tbb"
@@ -2235,4 +2427,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "eb29a33c1e0790dd3e5994de9d19d43925bba3f60c96ec42f5dfe0ed9b90d639"
+content-hash = "da625633f8712d546c49f6cf0d8ffa3d72a0367f2c850358819d584339751a19"
diff --git a/pyproject.toml b/pyproject.toml
index 7851fe6..5079a3e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "mnist"
+name = "metis"
 version = "0.0.1"
 description = ""
 authors = ["Arnold Unterauer <46269201+Anemosx@users.noreply.github.com>"]
@@ -16,6 +16,7 @@ pydantic = "^2.7.4"
 requests = "^2.32.3"
 tqdm = "^4.66.4"
 black = "^24.4.2"
+gymnasium = {extras = ["box2d"], version = "^0.29.1"}
 
 [[tool.poetry.source]]
 name = "torch_cuda121"
diff --git a/src/mnist/data/PLACE_DATASET_HERE b/src/classification/data/PLACE_DATASET_HERE.txt
similarity index 100%
rename from src/mnist/data/PLACE_DATASET_HERE
rename to src/classification/data/PLACE_DATASET_HERE.txt
diff --git a/src/classification/models/model.pt b/src/classification/models/model.pt
new file mode 100644
index 0000000..3c9aeb9
Binary files /dev/null and b/src/classification/models/model.pt differ
diff --git a/src/mnist/train.py b/src/classification/train.py
similarity index 100%
rename from src/mnist/train.py
rename to src/classification/train.py
diff --git a/src/mnist/models/PLACE_MODELS_HERE b/src/general/__init__.py
similarity index 100%
rename from src/mnist/models/PLACE_MODELS_HERE
rename to src/general/__init__.py
diff --git a/src/general/utils/__init__.py b/src/general/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/general/utils/models/__init__.py b/src/general/utils/models/__init__.py
new file mode 100644
index 0000000..d46005c
--- /dev/null
+++ b/src/general/utils/models/__init__.py
@@ -0,0 +1,3 @@
+from .cnn import ConvNet
+
+__all__ = ["ConvNet"]
diff --git a/src/general/utils/models/cnn.py b/src/general/utils/models/cnn.py
new file mode 100644
index 0000000..eaa0075
--- /dev/null
+++ b/src/general/utils/models/cnn.py
@@ -0,0 +1,122 @@
+import torch
+from torch import nn
+
+
+class ConvNet(nn.Module):
+    """
+    A Convolutional Neural Network (ConvNet) for image classification tasks.
+
+    Parameters
+    ----------
+    input_size : int, optional
+        Number of input channels (default is 1).
+    hidden_conv_layers : list of int, optional
+        Number of filters for each hidden convolutional layer (default is [32, 64]).
+    hidden_layers : list of int, optional
+        Number of neurons for each hidden fully connected layer
+        (default is [7 * 7 * 64, 1000]).
+    num_classes : int, optional
+        Number of output classes (default is 10).
+    kernel_size : int, optional
+        Size of the convolutional kernels (default is 5).
+    stride : int, optional
+        Stride for the convolutional layers (default is 1).
+    padding : int, optional
+        Padding for the convolutional layers (default is 2).
+    max_pool_kernel_size : int, optional
+        Size of the max pooling kernels (default is 2).
+    max_pool_stride : int, optional
+        Stride for the max pooling layers (default is 2).
+
+    Attributes
+    ----------
+    conv : nn.Sequential
+        Sequential container for the convolutional layers.
+    drop_out : nn.Dropout
+        Dropout layer to prevent overfitting.
+    fc : nn.Sequential
+        Sequential container for the fully connected layers.
+    """
+
+    def __init__(
+        self,
+        input_size: int = 1,
+        hidden_conv_layers: list[int] = None,
+        hidden_layers: list[int] = None,
+        num_classes: int = 10,
+        kernel_size: int = 5,
+        stride: int = 1,
+        padding: int = 2,
+        max_pool_kernel_size: int = 2,
+        max_pool_stride: int = 2,
+    ):
+
+        super(ConvNet, self).__init__()
+
+        if hidden_conv_layers is None:
+            hidden_conv_layers = [32, 64]
+
+        if hidden_layers is None:
+            hidden_layers = [7 * 7 * 64, 1000]
+
+        conv_layers = [
+            nn.Conv2d(
+                input_size,
+                hidden_conv_layers[0],
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+            ),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=max_pool_kernel_size, stride=max_pool_stride),
+        ]
+
+        for in_channels, out_channels in zip(
+            hidden_conv_layers[:-1], hidden_conv_layers[1:]
+        ):
+            conv_layers.extend(
+                [
+                    nn.Conv2d(
+                        in_channels,
+                        out_channels,
+                        kernel_size=kernel_size,
+                        stride=stride,
+                        padding=padding,
+                    ),
+                    nn.ReLU(),
+                    nn.MaxPool2d(
+                        kernel_size=max_pool_kernel_size, stride=max_pool_stride
+                    ),
+                ]
+            )
+
+        self.conv = nn.Sequential(*conv_layers)
+
+        self.drop_out = nn.Dropout()
+
+        fc_layers = []
+        for in_channels, out_channels in zip(hidden_layers[:-1], hidden_layers[1:]):
+            fc_layers.extend([nn.Linear(in_channels, out_channels)])
+        fc_layers.append(nn.Linear(hidden_layers[-1], num_classes))
+
+        self.fc = nn.Sequential(*fc_layers)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """
+        Defines the forward pass of the network.
+
+        Parameters
+        ----------
+        inputs : torch.Tensor
+            Input tensor of shape (batch_size, input_size, height, width).
+
+        Returns
+        -------
+        torch.Tensor
+            Output tensor of shape (batch_size, num_classes).
+        """
+        x = self.conv(inputs)
+        x = x.reshape(x.size(0), -1)
+        x = self.drop_out(x)
+        out = self.fc(x)
+        return out
diff --git a/src/reinforcement_learning/models/model.pt b/src/reinforcement_learning/models/model.pt
new file mode 100644
index 0000000..caeddfd
Binary files /dev/null and b/src/reinforcement_learning/models/model.pt differ
diff --git a/src/reinforcement_learning/train.py b/src/reinforcement_learning/train.py
new file mode 100644
index 0000000..22b0a82
--- /dev/null
+++ b/src/reinforcement_learning/train.py
@@ -0,0 +1,721 @@
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+import gymnasium as gym
+from gymnasium import Env
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions import Categorical
+
+
+class DenseNet(nn.Module):
+    """
+    Fully connected Neural Network for reinforcement learning.
+
+    Parameters
+    ----------
+    state_dim : int
+        The dimensionality of the input state space.
+    action_dim : int, optional
+        The dimensionality of the output action space (default is 1).
+
+    Methods
+    -------
+    forward(inputs)
+        Forward pass through the network.
+    """
+
+    def __init__(self, state_dim: int, action_dim: int = 1):
+        """
+        Initialize the Fully connected Neural Network model.
+
+        This constructor initializes the fully connected layers and leaky relu
+        activation function.
+
+        Parameters
+        ----------
+        state_dim : int
+            The dimensionality of the input state space.
+        action_dim : int, optional
+            The dimensionality of the output action space (default is 1).
+        """
+
+        super(DenseNet, self).__init__()
+        self.fc1 = nn.Linear(state_dim, 64)
+        self.fc2 = nn.Linear(64, 64)
+        self.fc3 = nn.Linear(64, 64)
+        self.fc4 = nn.Linear(64, action_dim)
+        self.activation = nn.LeakyReLU()
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """
+        Perform the forward pass of the network.
+
+        Parameters
+        ----------
+        inputs : torch.Tensor
+            A batch of input states [batch_size, state_dim], where `batch_size`
+            is the number of observations in the batch.
+
+        Returns
+        -------
+        out : torch.Tensor
+            The output logits for each action of shape [batch_size, action_dim].
+        """
+
+        x = self.activation(self.fc1(inputs))
+        x = self.activation(self.fc2(x))
+        x = self.activation(self.fc3(x))
+        out = self.fc4(x)
+        return out
+
+
+class Memory:
+    """
+    A memory buffer for storing experience tuples and computing returns and advantages
+    for reinforcement learning algorithms. It supports optional reward adjustment and
+    uses Generalized Advantage Estimation (GAE) for calculating the advantages.
+
+    Parameters
+    ----------
+    gamma : float, optional
+        The discount factor for future rewards (default is 0.99).
+    gae_lambda : float, optional
+        The smoothing parameter for Generalized Advantage Estimation (default is 0.95).
+    adjust_rewards : bool, optional
+        Whether to adjust rewards by subtracting a fixed value (default is True).
+    device : str | torch.device | None, optional
+        The device on which tensors will be stored and computed (default is 'cpu').
+
+    Attributes
+    ----------
+    gamma : float
+        The discount factor for future rewards.
+    gae_lambda : float
+        The GAE lambda value for advantage calculation.
+    adjust_rewards : bool
+        Flag to indicate if rewards should be adjusted.
+    device : torch.device
+        The device tensors are stored on.
+    memory : list
+        A list of experience tuples collected during training.
+
+    Methods
+    -------
+    push(state, action, log_prob, reward, done, value)
+        Adds an experience tuple to the memory.
+    create_batch(last_value)
+        Processes the stored experiences into a batch for training.
+    compute_returns_advantages(last_value, rewards, dones, values)
+        Computes the returns and advantages using the GAE algorithm.
+    """
+
+    def __init__(
+        self,
+        gamma: float = 0.99,
+        gae_lambda: float = 0.95,
+        adjust_rewards: bool = True,
+        device: str | torch.device | None = "cpu",
+    ):
+        self.gamma = gamma
+        self.gae_lambda = gae_lambda
+        self.adjust_rewards = adjust_rewards
+        self.device = device
+        self.memory = []
+
+    def push(
+        self,
+        state: torch.Tensor,
+        action: torch.Tensor,
+        log_prob: torch.Tensor,
+        reward: float,
+        done: bool,
+        value: torch.Tensor,
+    ) -> None:
+        """
+        Stores an experience tuple in the memory.
+
+        Parameters
+        ----------
+        state : torch.Tensor
+            The state observed from the environment.
+        action : torch.Tensor
+            The action taken in the environment.
+        log_prob : torch.Tensor
+            The log probability of taking the action given the state.
+        reward : float
+            The reward received after taking the action.
+        done : bool
+            A boolean flag indicating whether the episode has ended.
+        value : torch.Tensor
+            The value estimate of the state.
+        """
+
+        # increase penalties for longer episodes
+        if self.adjust_rewards:
+            reward = reward - 0.3
+
+        # add experience
+        self.memory.append((state, action, log_prob, reward, done, value))
+
+    def create_batch(
+        self, last_value: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Processes the memory to create a training batch.
+
+        Parameters
+        ----------
+        last_value : torch.Tensor
+            The value estimate for the final state.
+
+        Returns
+        -------
+        states : torch.Tensor
+            The states collected from the environment.
+        actions : torch.Tensor
+            The actions taken in the environment.
+        old_log_probs : torch.Tensor
+            The log probabilities of taking the actions given the states.
+        returns : torch.Tensor
+            The returns for the agent according to GAE.
+        advantages : torch.Tensor
+            The advantages for the agent according to GAE.
+        """
+
+        # unpack experience
+        states, actions, log_probs, rewards, dones, values = zip(*self.memory)
+
+        # format data
+        old_log_probs = torch.cat(log_probs)
+        states = torch.stack(states)
+        actions = torch.stack(actions).unsqueeze(1)
+
+        # calculate returns and advantages according to GAE
+        returns, advantages = self.compute_returns_advantages(
+            last_value, rewards, dones, values
+        )
+
+        # clear memory experience
+        self.memory = []
+
+        return states, actions, old_log_probs, returns, advantages
+
+    def compute_returns_advantages(
+        self,
+        last_value: torch.Tensor,
+        rewards: tuple[float],
+        dones: tuple[bool],
+        values: tuple[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Computes the returns and advantages using GAE.
+
+        Parameters
+        ----------
+        last_value : torch.Tensor
+            The value estimate for the final state.
+        rewards : tuple[float]
+            A tuple of rewards collected during the episode.
+        dones : tuple[bool]
+            A tuple of boolean flags indicating if each step is terminal.
+        values : tuple[torch.Tensor]
+            A tuple of value estimates at each step.
+
+        Returns
+        -------
+        returns : torch.Tensor
+            The returns for the agent according to GAE.
+        advantages : torch.Tensor
+            The advantages for the agent according to GAE.
+        """
+
+        with torch.no_grad():
+
+            # convert data to tensors
+            rewards = torch.tensor(rewards, device=self.device)
+            dones = torch.tensor(dones, device=self.device)
+            values = torch.tensor(values, device=self.device)
+
+            # initialize returns and advantages
+            returns = torch.zeros(rewards.shape, device=self.device)
+            advantages = torch.zeros(rewards.shape, device=self.device)
+
+            # compute the returns and advantages using GAE
+            gae = 0
+            next_value = last_value
+            for i in reversed(range(len(rewards))):
+                if dones[i]:
+                    next_value = 0
+                delta = rewards[i] + self.gamma * next_value - values[i]
+                gae = delta + self.gamma * self.gae_lambda * gae
+                next_value = values[i]
+                returns[i] = gae + values[i]
+                advantages[i] = gae
+
+            # normalize advantages
+            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+        return returns, advantages
+
+
+class PPOAgent:
+    """
+    A Proximal Policy Optimization (PPO) agent, implementing a policy gradient method
+    for reinforcement learning.
+
+    Parameters
+    ----------
+    state_dim : int | tuple[int]
+        The dimensionality of the state inputs.
+    action_dim : int
+        The number of discrete actions the agent can take.
+    policy_lr : float, optional
+        The learning rate for the policy optimizer (default is 4e-4).
+    value_lr : float, optional
+        The learning rate for the value optimizer (default is 2e-3).
+    opt_eps : float, optional
+        The epsilon term for numerical stability in the RMSprop optimizer
+        (default is 1e-6).
+    clip_eps : float, optional
+        The clipping range epsilon, used to limit the ratio of new to old
+        policy probabilities (default is 0.2).
+    clip_grad : float, optional
+        The gradient clipping value to prevent excessively large gradients during
+        backpropagation (default is 1).
+    update_epochs : int, optional
+        The number of epochs to use for each update cycle (default is 10).
+    entropy_coef : float, optional
+        Coefficient for entropy bonus added to the loss function (default is 0.001).
+    device : str | torch.device | None, optional
+        The device on which tensors will be processed (default is 'cpu').
+
+    Attributes
+    ----------
+    policy_net : DenseNet
+        The neural network model that determines the policy.
+    value_net : DenseNet
+        The neural network model that estimates state values.
+    policy_optimizer : optim.RMSprop
+        The optimizer for updating the policy network.
+    value_optimizer : optim.RMSprop
+        The optimizer for updating the value network.
+    MseLoss : nn.MSELoss
+        The mean squared error loss function used for value estimation.
+    """
+
+    def __init__(
+        self,
+        state_dim: int | tuple[int],
+        action_dim: int,
+        policy_lr: float = 4e-4,
+        value_lr: float = 2e-3,
+        opt_eps: float = 1e-6,
+        clip_eps: float = 0.2,
+        clip_grad: float = 1,
+        update_epochs: int = 10,
+        entropy_coef: float = 0.001,
+        device: str | torch.device | None = "cpu",
+    ):
+        self.state_dim = state_dim
+        self.action_dim = action_dim
+        self.device = device
+
+        # initialize policy and value networks and optimizers
+        self.policy_net = DenseNet(self.state_dim, self.action_dim).to(self.device)
+        self.value_net = DenseNet(self.state_dim).to(self.device)
+        self.policy_optimizer = optim.RMSprop(
+            self.policy_net.parameters(), lr=policy_lr, eps=opt_eps
+        )
+        self.value_optimizer = optim.RMSprop(
+            self.value_net.parameters(), lr=value_lr, eps=opt_eps
+        )
+
+        self.clip_eps = clip_eps
+        self.clip_grad = clip_grad
+        self.update_epochs = update_epochs
+        self.entropy_coef = entropy_coef
+
+        # loss criterion
+        self.MseLoss = nn.MSELoss()
+
+    def select_action(
+        self, state: np.ndarray | torch.Tensor, estimate_value: bool = True
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
+        """
+        Selects an action based on the current policy.
+
+        Parameters
+        ----------
+        state : np.ndarray | torch.Tensor
+            The current state from the environment.
+        estimate_value : bool, optional
+            Whether to estimate the value of the current state (default is True).
+
+        Returns
+        -------
+        torch.Tensor
+            The selected action as a tensor.
+        torch.Tensor
+            The log probability of the selected action.
+        torch.Tensor | None
+            The estimated value of the state or None if estimate_value is False.
+        """
+
+        with torch.no_grad():
+
+            # convert to tensor
+            if not isinstance(state, torch.Tensor):
+                state = torch.tensor(state, device=self.device)
+
+            # compute the action and log probability according to the policy
+            logits = self.policy_net(state.unsqueeze(0))
+            dist = Categorical(logits=logits)
+            action = dist.sample()
+            log_prob = dist.log_prob(action)
+
+            # compute the state value according to the value network
+            value = None
+            if estimate_value:
+                value = self.value_net(state)
+
+        return action, log_prob, value
+
+    def update(
+        self,
+        states: torch.Tensor,
+        actions: torch.Tensor,
+        old_log_probs: torch.Tensor,
+        returns: torch.Tensor,
+        advantages: torch.Tensor,
+    ) -> None:
+        """
+        Updates the policy and value networks based on provided training data.
+
+        Parameters
+        ----------
+        states : torch.Tensor
+            Tensor of all collected state observations.
+        actions : torch.Tensor
+            Tensor of all actions taken.
+        old_log_probs : torch.Tensor
+            Tensor of log probabilities of each action taken, as computed
+            by the old policy.
+        returns : torch.Tensor
+            Tensor of calculated returns.
+        advantages : torch.Tensor
+            Tensor of calculated advantages.
+        """
+
+        # train/optimize the agent for update_epochs times
+        for _ in range(self.update_epochs):
+
+            # compute current logits, distribution and
+            # log probabilities given the current policy
+            logits = self.policy_net(states)
+            dist = Categorical(logits=logits)
+            new_log_probs = dist.log_prob(actions.squeeze())
+
+            # compute the ratio of the new log probabilities to the old ones
+            ratios = torch.exp(new_log_probs - old_log_probs)
+
+            # calculate the first surrogate loss (unclipped)
+            surr1 = ratios * advantages
+
+            # calculate the second surrogate loss (clipped)
+            surr2 = (
+                torch.clamp(ratios, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
+            )
+
+            # compute the entropy loss to encourage exploration
+            loss_entropy = self.entropy_coef * dist.entropy().mean()
+
+            # compute the value loss using mean squared error
+            loss_value = 0.5 * self.MseLoss(returns, self.value_net(states).squeeze())
+
+            # compute the total policy loss
+            loss_policy = -torch.min(surr1, surr2).mean() - loss_entropy
+
+            # adjust weights and optimize according to loss and optimizer
+            self.policy_optimizer.zero_grad()
+            loss_policy.backward()
+            torch.nn.utils.clip_grad_norm_(self.policy_net.parameters(), self.clip_grad)
+            self.policy_optimizer.step()
+
+            self.value_optimizer.zero_grad()
+            loss_value.backward()
+            torch.nn.utils.clip_grad_norm_(self.value_net.parameters(), self.clip_grad)
+            self.value_optimizer.step()
+
+
+def train_model(
+    env: Env,
+    agent: PPOAgent,
+    memory: Memory,
+    max_episodes: int = 1000,
+    max_steps: int = 700,
+    smooth_window: int = 10,
+) -> None:
+    """
+    Trains a PPO agent in a given environment using a specified memory buffer
+    to store experiences.
+
+    Parameters
+    ----------
+    env : Env
+        The environment in which the agent is trained, which must adhere
+        to the OpenAI Gym interface.
+    agent : PPOAgent
+        The PPO agent to be trained.
+    memory : Memory
+        The memory buffer used to store agent experiences.
+    max_episodes : int, optional
+        The maximum number of training episodes (default is 1000).
+    max_steps : int, optional
+        The maximum number of steps to execute per episode (default is 700).
+    smooth_window : int, optional
+        The number of episodes over which to smooth the displayed
+        reward curve (default is 10).
+    """
+
+    episode_rewards = []
+    for episode in range(max_episodes):
+        # start new episode
+        state = env.reset()[0]
+        state = torch.tensor(state, device=agent.device)
+        episode_reward = 0
+
+        # play the episode
+        for t in range(max_steps):
+            # get the agent action, log probability and value of the state
+            action, log_prob, value = agent.select_action(state)
+
+            # execute the action in the environment
+            next_state, reward, done, _, _ = env.step(action.item())
+
+            # save the experience in the memory
+            memory.push(state, action, log_prob, reward, done, value)
+
+            # set new state
+            state = torch.tensor(next_state, device=agent.device)
+            episode_reward += reward
+
+            # stop early if environment done
+            if done:
+                break
+
+        # train the agent
+        agent.update(*memory.create_batch(agent.value_net(state)))
+
+        episode_rewards.append(episode_reward)
+        print(f"Episode {episode + 1}/{max_episodes}, Reward: {episode_reward}")
+
+    # plot the episode rewards during the training
+    smoothed_rewards = np.convolve(
+        episode_rewards, np.ones(smooth_window) / smooth_window, mode="valid"
+    )
+    plt.plot(smoothed_rewards)
+    plt.xlabel("Episodes")
+    plt.ylabel("Rewards")
+    plt.title("Episode Rewards")
+    plt.show()
+
+
+def evaluate_model(
+    env: Env, agent: PPOAgent, num_episodes: int = 10, max_steps: int = 800
+) -> None:
+    """
+    Evaluates a trained PPO agent in a specified environment over a given number
+    of episodes.
+
+    Parameters
+    ----------
+    env : Env
+        The environment in which the agent is evaluated, which must adhere
+        to the OpenAI Gym interface.
+    agent : PPOAgent
+        The trained PPO agent to be evaluated.
+    num_episodes : int, optional
+        The number of episodes for evaluation (default is 10).
+    max_steps : int, optional
+        The maximum number of steps to execute per episode during evaluation
+        (default is 800).
+    """
+
+    # play the evaluation episodes
+    for episode in range(num_episodes):
+        # start new episode
+        state = env.reset()[0]
+        episode_reward = 0
+
+        for t in range(max_steps):
+            # render the environment
+            env.render()
+
+            # get the agent action
+            action, _, _ = agent.select_action(state, estimate_value=False)
+
+            # execute the action in the environment
+            state, reward, done, _, _ = env.step(action.item())
+
+            episode_reward += reward
+            if done:
+                print(
+                    f"Evaluation Episode {episode + 1}/{num_episodes} finished with reward: {episode_reward}"
+                )
+                break
+
+
+def save_model(
+    policy_net: nn.Module, value_net: nn.Module, filename: str = None
+) -> None:
+    """
+    Saves the states of the agent's policy and value networks to a file.
+
+    Parameters
+    ----------
+    policy_net : nn.Module
+        The policy network to be saved.
+    value_net : nn.Module
+        The value network to be saved.
+    filename : str, optional
+        The path to the file where the network states should be saved.
+    """
+
+    if filename is None:
+        filename = os.path.join(os.getcwd(), "models", "model.pt")
+
+    torch.save(
+        {
+            "policy_net_state_dict": policy_net.state_dict(),
+            "value_net_state_dict": value_net.state_dict(),
+        },
+        filename,
+    )
+
+    print(f"Model saved to {filename}")
+
+
+def load_model(
+    policy_net: nn.Module, value_net: nn.Module, filename: str = None
+) -> None:
+    """
+    Loads the states of the agent's policy and value networks from a file.
+
+    Parameters
+    ----------
+    policy_net : nn.Module
+        The policy network into which the state should be loaded.
+    value_net : nn.Module
+        The value network into which the state should be loaded.
+    filename : str, optional
+        The path to the file from which the network states should be loaded.
+    """
+
+    if filename is None:
+        filename = os.path.join(os.getcwd(), "models", "model.pt")
+
+    checkpoint = torch.load(filename)
+    policy_net.load_state_dict(checkpoint["policy_net_state_dict"])
+    value_net.load_state_dict(checkpoint["value_net_state_dict"])
+
+    print(f"Model loaded from {filename}")
+
+
+def run() -> None:
+    """
+    Executes the full pipeline for training, saving, loading and evaluating
+    a reinforcement learning agent on an OpenAI Gym environment.
+    """
+
+    # set device for the computation
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # hyperparameters
+    gamma = 0.99
+    gae_lambda = 0.95
+
+    policy_lr = 4e-4
+    value_lr = 2e-3
+    opt_eps = 1e-6
+    clip_eps = 0.2
+    clip_grad = 1
+    update_epochs = 10
+    entropy_coef = 0.001
+
+    max_episodes = 1000
+    max_steps = 700
+    smooth_window = 10
+
+    eval_num_episodes = 10
+    eval_max_steps = 800
+
+    # initialize the train environment
+    train_env: Env = gym.make("LunarLander-v2", enable_wind=True, wind_power=5)
+
+    # setup input and output dims
+    state_dim: int = train_env.observation_space.shape[0]
+    action_dim: int = train_env.action_space.n
+
+    # initialize the agent
+    agent = PPOAgent(
+        state_dim,
+        action_dim,
+        policy_lr=policy_lr,
+        value_lr=value_lr,
+        opt_eps=opt_eps,
+        clip_eps=clip_eps,
+        clip_grad=clip_grad,
+        update_epochs=update_epochs,
+        entropy_coef=entropy_coef,
+        device=device,
+    )
+
+    # initialize the agent memory
+    memory = Memory(gamma=gamma, gae_lambda=gae_lambda, device=device)
+
+    # train the model
+    train_model(
+        train_env,
+        agent,
+        memory,
+        max_episodes=max_episodes,
+        max_steps=max_steps,
+        smooth_window=smooth_window,
+    )
+
+    train_env.close()
+
+    # save weights
+    save_model(agent.policy_net, agent.value_net)
+
+    # initialize and load model independently of training
+    agent = PPOAgent(
+        state_dim,
+        action_dim,
+        policy_lr=policy_lr,
+        value_lr=value_lr,
+        opt_eps=opt_eps,
+        clip_eps=clip_eps,
+        clip_grad=clip_grad,
+        update_epochs=update_epochs,
+        entropy_coef=entropy_coef,
+        device=device,
+    )
+    load_model(agent.policy_net, agent.value_net)
+
+    # initialize the test environment
+    eval_env: Env = gym.make("LunarLander-v2", render_mode="human")
+
+    # evaluate the model
+    evaluate_model(
+        eval_env, agent, num_episodes=eval_num_episodes, max_steps=eval_max_steps
+    )
+
+    eval_env.close()
+
+
+if __name__ == "__main__":
+    run()