a2c-PandaReachDense-v3

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Adilbai
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Quick Summary

This repository contains a trained Advantage Actor-Critic (A2C) reinforcement learning agent designed to solve the PandaReachDense-v3 environment from PyBullet Gym.

Code Examples

Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagebash
pip install stable-baselines3[extra]
pip install huggingface-sb3
pip install pybullet
pip install gym
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()
Usagepython
import gym
import pybullet_envs
from stable_baselines3 import A2C
from huggingface_sb3 import load_from_hub

# Load the trained model
model = load_from_hub(
    repo_id="Adilbai/a2c-PandaReachDense-v3",
    filename="a2c-PandaReachDense-v3.zip"
)

# Create the environment
env = gym.make("PandaReachDense-v3")

# Evaluate the model
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()  # Optional: visualize the agent
    if done:
        obs = env.reset()

env.close()

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