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Deep Reinforcement Learning Assignment Help, Project Help & Homework Help

Get help In World Models

Here we understand some generative neural network models of popular reinforcement learning environments in which you can get help from our Reinforcement Learning experts. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial.


Agent Model

In this model, our agent has a visual sensory component that compresses what it sees into a small representative code. It also has a memory component that makes predictions about future codes based on historical information. Finally, our agent has a decision-making component that decides what actions to take based only on the representations created by its vision and memory components.


Our agent consists of three components that work closely together: Vision (V), Memory (M), and Controller (C).


VAE (V) Model

The environment provides our agent with a high dimensional input observation at each time step. This input is usually a 2D image frame that is part of a video sequence.


MDN-RNN (M) Model

The M model serves as a predictive model of the future z vectors that V is expected to produce. Because many complex environments are stochastic in nature, we train our RNN to output a probability density function p(z) instead of a deterministic prediction of z.


Controller (C) Model

The Controller (C) model is responsible for determining the course of actions to take in order to maximize the expected cumulative reward of the agent during a rollout of the environment.


Below is the pseudocode for how our agent model is used in the OpenAI Gym environment. Running this function on a given controller C will return the cumulative reward during a rollout of the environment.


def rollout(controller):
  ''' env, rnn, vae are '''
  ''' global variables  '''
  obs = env.reset()
  h = rnn.initial_state()
  done = False
  cumulative_reward = 0
  while not done:
    z = vae.encode(obs)
    a = controller.action([z, h])
    obs, reward, done = env.step(a)
    cumulative_reward += reward
    h = rnn.forward([a, z, h])
return cumulative_reward


Important Third Party Reinforcement Learning environments

Video Game environments

Supported fork of gym-retro with additional games, states, scenarios, etc. Open to PRs of additional games, features, and platforms since gym-retro is no longer maintained


A simple environment for single-agent reinforcement learning algorithms on a clone of Flappy Bird, the hugely popular arcade-style mobile game. Both state and pixel observation environments are available.


This is a 3v3 MOBA environment where you train creatures to fight each other. It runs entirely on the GPU so you can easily have hundreds of instances running in parallel. There are around 15 items for the creatures, 60 “senses”, 5 actions, and roughly 23 tweakable rewards. It’s also possible to benchmark an agent against other agents online. It’s available for free for training for personal use, and otherwise costs money; see licensing details on the website


A simple environment for benchmarking single and multi-agent reinforcement learning algorithms on a clone of Slime Volleyball game. The only dependencies are gym and NumPy. Both state and pixel observation environments are available. The motivation of this environment is to easily enable trained agents to play against each other, and also facilitate the training of agents directly in a multi-agent setting, thus adding an extra dimension for evaluating an agent’s performance.


Gym wrappers for arbitrary and premade environments with the Unity game engine.


PGE is a FOSS 3D engine for AI simulations and can interoperate with the Gym. Contains environments with modern 3D graphics, and uses Bullet for physics.



Robotics environments

gym-jiminy presents an extension of the initial Gym for robotics using Jiminy, an extremely fast and light-weight simulator for poly-articulated systems using Pinocchio for physics evaluation and Meshcat for web-based 3D rendering.


A simple environment using PyBullet to simulate the dynamics of a Bitcraze Crazyflie 2.x nanoquadrotor.


Mars Explorer is a Gym compatible environment designed and developed as an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of an unknown terrain.


PyBullet based simulations of a robotic arm moving objects.


Robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real-world robotics.


Gym environments that let you control physics robotics in a laboratory via the internet.


PyBullet based CartPole and Quadrotor environments—with CasADi (symbolic) a priori dynamics and constraints—for learning-based control and model-based reinforcement learning.


A large-scale benchmark for co-optimizing the design and control of soft robots.


A Simulation Environment to train Robots in Large Realistic Interactive Scenes


This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym


Reinforcement Learning Environments for Omniverse Isaac Gym



Autonomous Driving environments

Gym wrapper for various environments in the Sumo traffic simulator


A lane-following simulator built for the Duckietown project (small-scale self-driving car course).


An environment for simulating a wide variety of electric drives taking into account different types of electric motors and converters. Control schemes can be continuous, yielding a voltage duty cycle, or discrete, determining converter switching states directly.


An environment for behavioral planning in autonomous driving, with an emphasis on high-level perception and decision rather than low-level sensing and control. The difficulty of the task lies in understanding the social interactions with other drivers, whose behaviors are uncertain. Several scenes are proposed, such as highway, merge, intersection and roundabout.


A Gym for solving motion planning problems for various traffic scenarios compatible with CommonRoad benchmarks, which provides configurable rewards, action spaces, and observation spaces.


tmrl is a distributed framework for training Deep Reinforcement Learning AIs in real-time applications. It is demonstrated on the TrackMania 2020 video game.


Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing


An open-source reinforcement learning environment for autonomous racing.


A gym environment for a miniature racecar using the pybullet physics engine.




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