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Kyunghwan Kim
ecb42d30d2
* Add per and modify etc * Replace pre-commit-config.yaml and add pre-commit hook in .git * Modify .gitignore * Modify .gitignore * Modify buffer and code * Modify replay buffer and per * Modify .gitignore
109 lines
2.6 KiB
Python
109 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
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"""Run module for DDPG on LunarLanderContinuous-v2.
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- Author: Curt Park
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- Contact: curt.park@medipixel.io
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"""
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import argparse
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import gym
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import torch
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import torch.optim as optim
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from algorithms.common.networks.mlp import MLP
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from algorithms.common.noise import OUNoise
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from algorithms.ddpg.agent import Agent
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# hyper parameters
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hyper_params = {
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"GAMMA": 0.99,
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"TAU": 1e-3,
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"BUFFER_SIZE": int(1e5),
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"BATCH_SIZE": 128,
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"LR_ACTOR": 1e-3,
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"LR_CRITIC": 1e-3,
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"OU_NOISE_THETA": 0.0,
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"OU_NOISE_SIGMA": 0.0,
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"WEIGHT_DECAY": 1e-6,
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}
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def run(env: gym.Env, args: argparse.Namespace, state_dim: int, action_dim: int):
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"""Run training or test.
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Args:
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env (gym.Env): openAI Gym environment with continuous action space
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args (argparse.Namespace): arguments including training settings
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state_dim (int): dimension of states
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action_dim (int): dimension of actions
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"""
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hidden_sizes_actor = [256, 256]
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hidden_sizes_critic = [256, 256]
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# create actor
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actor = MLP(
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input_size=state_dim,
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output_size=action_dim,
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hidden_sizes=hidden_sizes_actor,
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output_activation=torch.tanh,
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).to(device)
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actor_target = MLP(
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input_size=state_dim,
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output_size=action_dim,
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hidden_sizes=hidden_sizes_actor,
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output_activation=torch.tanh,
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).to(device)
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actor_target.load_state_dict(actor.state_dict())
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# create critic
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critic = MLP(
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input_size=state_dim + action_dim,
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output_size=1,
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hidden_sizes=hidden_sizes_critic,
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).to(device)
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critic_target = MLP(
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input_size=state_dim + action_dim,
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output_size=1,
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hidden_sizes=hidden_sizes_critic,
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).to(device)
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critic_target.load_state_dict(critic.state_dict())
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# create optimizer
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actor_optim = optim.Adam(
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actor.parameters(),
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lr=hyper_params["LR_ACTOR"],
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weight_decay=hyper_params["WEIGHT_DECAY"],
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)
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critic_optim = optim.Adam(
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critic.parameters(),
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lr=hyper_params["LR_CRITIC"],
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weight_decay=hyper_params["WEIGHT_DECAY"],
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)
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# noise
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noise = OUNoise(
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action_dim,
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theta=hyper_params["OU_NOISE_THETA"],
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sigma=hyper_params["OU_NOISE_SIGMA"],
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)
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# make tuples to create an agent
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models = (actor, actor_target, critic, critic_target)
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optims = (actor_optim, critic_optim)
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# create an agent
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agent = Agent(env, args, hyper_params, models, optims, noise)
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# run
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if args.test:
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agent.test()
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else:
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agent.train()
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