mirror of
https://github.com/wassname/kair_algorithms_draft.git
synced 2026-10-04 12:40:46 +08:00
Seungjae Ryan Lee
ca5c99bc41
* Format repository * Clone files from medipixel repo * Fix DDPGfDAgent.update_model() * Fix bug on _initialize() * Add demo-path parameter and demo data * Rename init_priority to _max_priority for PER This makes PER and PERfD consistent. * Make i_episode attribute of DDPGAgent * Clone SAC code from medipixel repo * Fix update_model() for SACfD * Fix _initialize() for SACfD * Add is_discrete attribute to AbstractAgent for SACfD * Add i_episode attribute to SACAgent for SACfD * Modularize DDPGAgent and SACAgent * Modify hyperparameters for DDPGfD and SACfD * Add NStepBuffer * Add n-step to DDPGfD * Add n-step to SACfD * Add TD3fD without n-step * Attempt to tune hyperparameters * Remove discrete environment check in SAC * Implement n-step on TD3fD * Fix step function of TD3 No done check, and _add_transition_to_memory was not called. * Fix actor loss calculation for TD3fD * Attempt to tune hyperparameters * Print both critic losses * Fix typo bug * Attempt to tune hyperparameters * Fix bug in n-step demo retrieval * Fix bug in n-step transition addition
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
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"""Run module for SACfD 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 numpy as np
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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, FlattenMLP, TanhGaussianDistParams
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from algorithms.fd.sac_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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"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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"AUTO_ENTROPY_TUNING": True,
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"LR_ACTOR": 3e-4,
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"LR_VF": 3e-4,
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"LR_QF1": 3e-4,
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"LR_QF2": 3e-4,
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"LR_ENTROPY": 3e-4,
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"W_ENTROPY": 1e-3,
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"W_MEAN_REG": 1e-3,
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"W_STD_REG": 1e-3,
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"W_PRE_ACTIVATION_REG": 0.0,
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"DELAYED_UPDATE": 2,
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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": 0.6,
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"PER_BETA": 0.4,
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"PER_EPS": 1e-6,
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"PER_EPS_DEMO": 1.0,
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"INITIAL_RANDOM_ACTION": int(5e3),
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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_vf = [256, 256]
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hidden_sizes_qf = [256, 256]
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# target entropy
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target_entropy = -np.prod((action_dim,)).item() # heuristic
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# create actor
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actor = TanhGaussianDistParams(
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input_size=state_dim, output_size=action_dim, hidden_sizes=hidden_sizes_actor
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).to(device)
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# create v_critic
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vf = MLP(input_size=state_dim, output_size=1, hidden_sizes=hidden_sizes_vf).to(
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device
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)
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vf_target = MLP(
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input_size=state_dim, output_size=1, hidden_sizes=hidden_sizes_vf
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).to(device)
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vf_target.load_state_dict(vf.state_dict())
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# create q_critic
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qf_1 = FlattenMLP(
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input_size=state_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes_qf
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).to(device)
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qf_2 = FlattenMLP(
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input_size=state_dim + action_dim, output_size=1, hidden_sizes=hidden_sizes_qf
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).to(device)
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# create optimizers
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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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vf_optim = optim.Adam(
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vf.parameters(), lr=hyper_params["LR_VF"], weight_decay=hyper_params["LAMBDA2"]
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)
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qf_1_optim = optim.Adam(
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qf_1.parameters(),
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lr=hyper_params["LR_QF1"],
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weight_decay=hyper_params["LAMBDA2"],
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)
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qf_2_optim = optim.Adam(
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qf_2.parameters(),
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lr=hyper_params["LR_QF2"],
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weight_decay=hyper_params["LAMBDA2"],
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)
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# make tuples to create an agent
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models = (actor, vf, vf_target, qf_1, qf_2)
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optims = (actor_optim, vf_optim, qf_1_optim, qf_2_optim)
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# create an agent
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agent = Agent(env, args, hyper_params, models, optims, target_entropy)
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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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