Reinforcement Learning with Model-Based Policy Optimization (MBPO)

This repository contains the implementation of Model-Based Policy Optimization (MBPO) algorithm for reinforcement learning. MBPO is an off-policy model-based reinforcement learning algorithm that uses an ensemble of models to learn dynamics and a policy to optimize control. It combines the benefits of model-based RL (sample efficiency) and model-free RL (policy optimization). The algorithm achieves state-of-the-art performance on several continuous control benchmark tasks.

Installation

To run the code in this repository, you need to have Python 3.6 or later installed on your system. You also need to install the following dependencies:

• PyTorch: The deep learning library used for implementing the neural network models.
• NumPy: A library for numerical computing in Python.
• OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms.
• Matplotlib: A plotting library for creating visualizations in Python.

You can install the dependencies using the following command:

pip install torch numpy gym matplotlib

Usage

To use the MBPO algorithm, you need to import the necessary modules and classes from the code. Here is an example of how to use the EnsembleModel class for training and testing an agent:

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from mbpo import EnsembleModel, ReplayBuffer

# Create an instance of the model
input_dims = (state_dim,)
n_actions = action_dim
hidden_dims = 200
alpha = 0.001
weight_decay = 1e-4
n_models = 5
n_elites = 2
batch_size = 256
model = EnsembleModel(alpha, input_dims, n_actions, weight_decay, n_models, n_elites, hidden_dims, batch_size)

# Create an instance of the replay buffer
buffer_size = int(1e6)
replay_buffer = ReplayBuffer(input_dims, n_actions, buffer_size)

# Training loop
for episode in range(num_episodes):
    state = env.reset()
    done = False

    while not done:
        # Collect data using the current policy
        action = model.get_action(state)
        next_state, reward, done, _ = env.step(action)

        # Store the transition in the replay buffer
        replay_buffer.store_transition(state, action, reward, next_state, done)

        # Update the model using MBPO
        model.update(replay_buffer)

        state = next_state

# Testing loop
for episode in range(num_episodes):
    state = env.reset()
    done = False

    while not done:
        # Get action from the model
        action = model.get_action(state)

        # Take a step in the environment
        next_state, reward, done, _ = env.step(action)

        state = next_state

You can customize the hyperparameters and other settings according to your needs. The above code provides a basic example of how to use the MBPO algorithm with the provided classes.

Contributing

Contributions to this repository are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

References

• Janner M, Fu J, Zhang M, Levine S. When to trust your model: Model-based policy optimization. Advances in neural information processing systems. 2019;32.