Gist c9d1f7872865db4e5bfc0991636376c8

100%

Gist c9d1f7872865db4e5bfc0991636376c8

✓ Published0🌍 Public

Last edited Jun 9, 2016

Created on Jun 9, 2016

This example visualizes the training progress of a Dueling Double Deep Q-Network (Duel DDQN) agent across 10,000 episodes in the MountainCar-v0 environment. It uses the Python library `duel.py` from the Duel_DDQN repository, invoking it via a command-line call with the `--gym_record` flag to capture the agent’s performance over time. The chart presents episode-by-episode reward trends, showing how the agent’s policy improves as training advances, with the underlying data generated by OpenAI Gym’s environment simulation.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 7814a13dbae9fde1771606fe6081513d

This interactive visualization tracks the training progress of a Dueling Double Deep Q-Network (Duel DDQN) agent within the OpenAI Gym CartPole-v0 environment. It plots the reward per episode over time, showing how the agent's performance improves as it learns to balance the pole. The code uses the Python libraries `gym` for the environment and `torch` for the neural network, with command-line arguments specifying hyperparameters like `--gamma` and `--batch_size`. The chart updates in real time, rendering the learning curve as the agent iterates through episodes.

SSohojoe

88% match

Loading thumbnail…

kerlym pong.py does not work with default settings

This visualization compares the training progress of DQN and DDQN agents over 55,000 episodes of the Atari Pong-v0 environment, showing that neither algorithm converges to a winning strategy. The plot uses Gym’s evaluation API to fetch reward-per-episode data from two public evaluation records. The chart displays raw episode rewards as thin gray lines with a bold moving average overlay, directly exposing the high variance and lack of improvement in both agents’ performance. The code uses Python with matplotlib for plotting and requests to load JSON data from the Gym evaluation endpoints.

SSohojoe

66% match

Loading thumbnail…

dyna-maze based on https://github.com/thehawkgriffith/dyna-maze

This example animates a reinforcement-learning agent navigating a 6×9 grid maze toward a goal state, using the Dyna-Q algorithm to balance exploration and planning. The visualization shows the agent’s current position as a marker, a live policy heatmap of action values, and the cumulative reward per episode. Built with Matplotlib and NumPy, the scene renders in a terminal via `Environment.render()`, while the agent’s state and action history drive the displayed trajectories. The maze layout, obstacles, and reward structure come from the `dyna-maze` repository’s hardcoded environment.

SSohojoe

60% match

Loading thumbnail…

Untitled

This interactive visualization shows how a deep reinforcement learning agent’s performance evolves over training timesteps, with multiple score series tracking different agents or runs. The chart animates through the training timeline, allowing viewers to compare score trajectories and observe how performance improves over time. Built with D3.js v7, the SVG-based visualization uses a slider to scrub through timesteps and dynamically update the line chart. The data comes from a hardcoded score list, and the page includes tabbed navigation and buttons to switch between overview and detailed views of the training process.

Llloydy8

60% match

Loading thumbnail…

OpenAi.Gym.Baselines.DeepQ Restoring checkpoint in next training

This example demonstrates a continuous training loop for OpenAI Gym’s CartPole-v0 using the Baselines DeepQ algorithm. It shows how a model checkpoint is saved and restored across alternating training and evaluation sessions, allowing training to resume seamlessly. The code uses `deepq.learn` and `deepq.load` from Baselines, with a custom `ActWrapper` that serializes the model via dill and TensorFlow’s `U.save_state`. A shell script alternates between `train_worker.py` and `enjoy_worker.py`, pausing training to render episodes and report rewards before resuming.

SSohojoe

57% match

Loading thumbnail…

Training Performance

The visualization shows training progress across four reinforcement-learning environments—Hopper, Walker, Humanoid, and Ant—by comparing their total training steps, wall-clock time, and throughput. It uses a table-style layout rendered with plain HTML and CSS, drawing data from a TSV file. Each environment row displays its metrics, with the steps-per-second value visually encoded as a horizontal bar, making the relative performance of each task immediately comparable. The code loads the data via `d3.tsv` and constructs the bar chart using SVG `rect` elements scaled by the parsed numeric values.

SSohojoe

54% match

Loading thumbnail…

Simple Neuroevolution Cartpole

This example shows a neuroevolution system training agents to solve the CartPole-v0 balancing task using OpenAI Gym. A population of 100 neural networks evolves over 100 epochs, with each network’s fitness determined by its average score across 5 episodes. The Keras models use densely connected layers with ReLU activation, while the evolution process selects and mutates networks based on their performance. Agent scores are collected and then the top performers are used to generate the next generation, gradually improving the population’s ability to keep the pole upright.

SSohojoe

54% match

Loading thumbnail…

Gist 6f46e33cf77d3c5298d440be532e3440

This example visualizes a chess subreddit’s weekly activity as a force-directed network, showing how posts and comments cluster around topics. The code uses D3’s `forceSimulation` for layout, with node positions updated via `tick` handlers. It renders the graph using SVG paths and applies color and size scales to represent post scores and comment counts. Data comes from the Reddit API, filtered by the `subreddit=chess` and `period=week` environment variables. The visualization also includes a play/pause button to freeze the simulation, and tooltips display metadata on hover. The underlying gist relies on Node.js and the `dotenv` library to load API credentials, while the frontend uses plain D3 for canvas-based interaction.

CConorAspell

54% match