Python Agent.run_reps Examples

Example #1

from environments.lqr import LQR
from models.simple import Simple
from policies.rand import RandomPolicy
from policies.normal import NormalPolicy
from utils.data import SARSDataset

random.seed(42)

environment = LQR(-1, 1)
policy_model_random = RandomPolicy(-2, 2)
policy_model_normal = NormalPolicy([1, 9, 1], [1.])
policy_model = policy_model_normal
value_model = Simple()

agent = Agent(environment, policy_model, value_model, verbose=True)
agent.run_reps(iterations=3)

state_space = np.arange(-2, 2.1, 0.1)
action_space = np.arange(-2, 2.1, 0.1)

# loop over state action pairs
observations = []
for state in state_space:
    for action in action_space:
        environment.state = state
        new_state, reward, _, _ = environment.step(action)
        observations.append({
            'prev_state': state,
            'action': action,
            'reward': reward,
            'new_state':new_state})

Example #2

File: atari.py Project: stefanknegt/REPS

import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

from agents.agent import Agent
from environments.atari import AtariBreakout
from policies.rand import RandomPolicy
from policies.normal import NormalPolicy
from models.mlp import MLP

environment = AtariBreakout()
# policy_model = RandomPolicy(0, 3, is_discrete=True)
policy_model = NormalPolicy([128, 64, 1], 1.)
value_model = MLP([128, 64, 1])

agent = Agent(environment, policy_model, value_model, verbose=True)
agent.run_reps(exp_render=True)

Example #3

File: cartpole.py Project: stefanknegt/REPS

import torch
from agents.agent import Agent
from environments.lqr import LQR
from models.simple import Simple
from policies.rand import RandomPolicy
from policies.normal import NormalPolicy
from models.mlp import MLP
from utils.data import SARSDataset

import gym
from torch.nn import functional as F

environment = LQR(-1, 1)
policy_model_normal = NormalPolicy([1, 20, 45, 1], [4 for i in range(1)],
                                   activation=F.tanh)
policy_model = policy_model_normal
value_model = MLP([1, 20, 45, 1])

random.seed(42)
torch.manual_seed(42)
np.random.seed(42)
#environment.seed(42)

agent = Agent(environment, policy_model, value_model, verbose=True)
agent.run_reps(100,
               exp_timesteps=1000,
               exp_episodes=10,
               exp_render=False,
               val_epsilon=0.1,
               pol_lr=1e-2)