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Sealswalker2d V0

Developed by ernestumorga
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the seals/Walker2d-v0 environment to control the walking task of the Walker2d robot.
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Release Time : 5/27/2022

Model Overview

This model is trained using the PPO algorithm in the Stable Baselines3 library and can achieve stable walking control in the seals/Walker2d-v0 environment.

Model Features

Efficient policy optimization
Use the PPO algorithm to achieve stable and efficient policy optimization, suitable for control tasks in continuous action spaces.
Custom network architecture
Adopt a two-layer MLP network structure with 256 nodes per layer, and the activation function is ReLU, which balances expressiveness and training efficiency.
Parameter optimization
A carefully tuned combination of hyperparameters, including key parameters such as learning rate and discount factor.

Model Capabilities

Continuous action space control
Robot motion control
Reinforcement learning policy optimization

Use Cases

Robot control
Bipedal robot walking
Control the bipedal robot to achieve stable walking motion
Average reward 1429.13 +/- 411.75
Reinforcement learning research
Algorithm performance comparison
Use as a baseline model to compare performance with other reinforcement learning algorithms
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