Here is an example of how to use the RL_Coach library in Python:
Installation
bash
pip install rl_coach
Basic Usage
python
import rl_coach
from rl_coach.agents.dqn_agent import DQNAgentParameters
from rl_coach.environments.gym_environment import GymVectorEnvironment
# Define the environment
env_params = GymVectorEnvironment(level='CartPole-v0')
# Define the agent
agent_params = DQNAgentParameters(
path_to_save_or_load_model='path_to_model',
num_concurrent_sequences=1,
num_steps_between_decay=100,
num_steps_to_start_decay=100,
num_steps_to_start_exploration=100,
exploration_type=rl_coach.exploration_types.EpsilonGreedy,
exploration_params=rl_coach.exploration_params.EpsilonGreedyExplorationParams(
epsilon=0.1,
min_epsilon=0.01,
epsilon_decay_rate=0.995
)
)
# Create the agent
agent = rl_coach.agents.dqn_agent.DQNAgent(agent_params)
# Create the environment
env = GymVectorEnvironment(env_params)
# Train the agent
agent.train(env, 1000)
Running Coach
bash
coach -p CartPole_DQN -r
Example Code for Custom Environment
python
import gym
import numpy as np
from rl_coach.environments.gym_environment import GymEnvironment
class MyEnvironment(gym.Env):
def __init__(self):
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(4,))
self.action_space = gym.spaces.Discrete(2)
def step(self, action):
# Implement your environment logic here
pass
def reset(self):
# Implement your environment logic here
pass
def render(self, mode='human'):
# Implement your environment logic here
pass
# Create the environment
env = MyEnvironment()
# Create the agent
agent = rl_coach.agents.dqn_agent.DQNAgent(agent_params)
# Train the agent
agent.train(env, 1000)
Example Code for Custom Agent
python
import rl_coach
from rl_coach.agents.dqn_agent import DQNAgentParameters
class MyAgent(rl_coach.agents.dqn_agent.DQNAgent):
def __init__(self):
super().__init__(agent_params)
def compute_q_values(self, observations):
# Implement your custom Q-value computation here
pass
def compute_target_values(self, observations, actions):
# Implement your custom target value computation here
pass
# Create the agent
agent = MyAgent()
# Create the environment
env = GymVectorEnvironment(env_params)
# Train the agent
agent.train(env, 1000)
Example Code for Custom Visualization
python
import matplotlib.pyplot as plt
from rl_coach.graph_managers.graph_manager import GraphManager
class MyVisualization:
def __init__(self):
self.graph_manager = GraphManager()
def visualize(self, data):
# Implement your custom visualization here
plt.plot(data)
plt.show()
# Create the visualization
vis = MyVisualization()
# Create the agent
agent = rl_coach.agents.dqn_agent.DQNAgent(agent_params)
# Create the environment
env = GymVectorEnvironment(env_params)
# Train the agent
agent.train(env, 1000)
# Visualize the results
vis.visualize(agent.get_results())
These examples demonstrate how to use RL_Coach to implement reinforcement learning algorithms, create custom environments and agents, and visualize results. For more detailed information, refer to the official documentation and tutorials provided with the library[1][2][3].
Citations:[1] https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python
[2] https://github.com/IntelLabs/coach
[3] https://intellabs.github.io/coach/_modules/rl_coach/environments/gym_environment.html
[4] https://winder.ai/a-comparison-of-reinforcement-learning-frameworks-dopamine-rllib-keras-rl-coach-trfl-tensorforce-coach-and-more/
[5] https://intellabs.github.io/coach/