What are some practical examples of using RL_Coach with OpenAI Gym

Example 1: CartPole-v1 with DQN

python
import rl_coach
from rl_coach.agents.dqn_agent import DQNAgentParameters
from rl_coach.environments.gym_environment import GymEnvironment

# Define the environment
env_params = GymEnvironment(level='CartPole-v1')

# 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 = GymEnvironment(env_params)

# Train the agent
agent.train(env, 1000)

Example 2: MountainCar-v0 with SAC

python
import rl_coach
from rl_coach.agents.sac_agent import SACAgentParameters
from rl_coach.environments.gym_environment import GymEnvironment

# Define the environment
env_params = GymEnvironment(level='MountainCar-v0')

# Define the agent
agent_params = SACAgentParameters(
    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.sac_agent.SACAgent(agent_params)

# Create the environment
env = GymEnvironment(env_params)

# Train the agent
agent.train(env, 1000)

Example 3: Pong-v5 with Double Dueling DQN

python
import rl_coach
from rl_coach.agents.dueling_dqn_agent import DuelingDQNAgentParameters
from rl_coach.environments.gym_environment import GymEnvironment

# Define the environment
env_params = GymEnvironment(level='Pong-v5')

# Define the agent
agent_params = DuelingDQNAgentParameters(
    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.dueling_dqn_agent.DuelingDQNAgent(agent_params)

# Create the environment
env = GymEnvironment(env_params)

# Train the agent
agent.train(env, 1000)

Example 4: 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 5: 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 = GymEnvironment(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][4][5].