mirror of
https://github.com/wassname/kair_algorithms_draft.git
synced 2026-10-04 12:40:46 +08:00
Kyunghwan Kim
d2769dfa9d
* Convert code format to python2.7 (SAC) * Convert code format python2.7 (TD3, all fD) * Remove no use import and black setting * Change SAC param * Change env name Reacher-v2 to v1 * Remove old version reacher training script * Convert code format python2.7 * Modify .travis.yml * Add install command python3.6 & black on Makefile * Fix seperator to tab on Makefile * Modify Makefile * Fix little error * Change td3 gamma parameter
145 lines
3.8 KiB
Python
145 lines
3.8 KiB
Python
# -*- coding: utf-8 -*-
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"""Run module for SACfD on LunarLanderContinuous-v2.
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- Author: Seungjae Ryan Lee
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- Contact: seungjaeryanlee@gmail.com
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"""
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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 GaussianNoise
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from algorithms.fd.td3_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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# TODO Tune hyperparameters on LunarLander-v2
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# hyper parameters
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hyper_params = {
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"N_STEP": 3,
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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": 64,
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"LR_ACTOR": 3e-4,
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"LR_CRITIC": 3e-4,
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"EXPLORATION_NOISE": 0.1,
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"TARGET_POLICY_NOISE": 0.2,
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"TARGET_POLICY_NOISE_CLIP": 0.5,
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"POLICY_UPDATE_FREQ": 2,
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"INITIAL_RANDOM_ACTIONS": 5e3,
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"PRETRAIN_STEP": 100,
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"MULTIPLE_LEARN": 2, # multiple learning updates
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"LAMBDA1": 1.0, # N-step return weight
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"LAMBDA2": 1e-5, # l2 regularization weight
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"LAMBDA3": 1.0, # actor loss contribution of prior weight
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"PER_ALPHA": 0.3,
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"PER_BETA": 1.0,
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"PER_EPS": 1e-6,
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"PER_EPS_DEMO": 1.0,
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}
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def run(env, args, state_dim, action_dim):
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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 = [400, 300]
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hidden_sizes_critic = [400, 300]
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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 critic1
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critic1 = 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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critic1_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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critic1_target.load_state_dict(critic1.state_dict())
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# create critic2
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critic2 = 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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critic2_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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critic2_target.load_state_dict(critic2.state_dict())
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# concat critic parameters to use one optim
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critic_parameters = list(critic1.parameters()) + list(critic2.parameters())
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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["LAMBDA2"],
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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["LAMBDA2"],
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)
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# noise
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exploration_noise = GaussianNoise(
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action_dim,
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min_sigma=hyper_params["EXPLORATION_NOISE"],
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max_sigma=hyper_params["EXPLORATION_NOISE"],
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)
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target_policy_noise = GaussianNoise(
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action_dim,
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min_sigma=hyper_params["TARGET_POLICY_NOISE"],
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max_sigma=hyper_params["TARGET_POLICY_NOISE"],
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)
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# make tuples to create an agent
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models = (actor, actor_target, critic1, critic1_target, critic2, critic2_target)
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optims = (actor_optim, critic_optim)
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noises = (exploration_noise, target_policy_noise)
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# create an agent
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agent = Agent(env, args, hyper_params, models, optims, noises)
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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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