Final environment class and test scripts before the test (#43)

* new user branch

* Resolve formatting issues on test scripts

* Resolve formatting issues on test scripts

scripts/examples/open_manipulator_reacher_v0/td3.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+"""Run module for TD3 on LunarLanderContinuous-v2.
+
+- Author: whikwon
+- Contact: whikwon@gmail.com
+"""
+
+import torch
+import torch.optim as optim
+from algorithms.common.networks.mlp import MLP
+from algorithms.common.noise import GaussianNoise
+from algorithms.td3.agent import Agent
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# hyper parameters
+hyper_params = {
+    "GAMMA": 0.99,
+    "TAU": 5e-3,
+    "BUFFER_SIZE": int(1e6),
+    "BATCH_SIZE": 100,
+    "LR_ACTOR": 1e-3,
+    "LR_CRITIC": 1e-3,
+    "WEIGHT_DECAY": 0.000,
+    "EXPLORATION_NOISE": 0.1,
+    "TARGET_POLICY_NOISE": 0.2,
+    "TARGET_POLICY_NOISE_CLIP": 0.5,
+    "POLICY_UPDATE_FREQ": 2,
+    "INITIAL_RANDOM_ACTIONS": 1e4,
+}
+
+
+def run(env, args, state_dim, action_dim):
+    """Run training or test.
+
+    Args:
+        env (gym.Env): openAI Gym environment with continuous action space
+        args (argparse.Namespace): arguments including training settings
+        state_dim (int): dimension of states
+        action_dim (int): dimension of actions
+
+    """
+    hidden_sizes_actor = [400, 300]
+    hidden_sizes_critic = [400, 300]
+
+    # create actor
+    actor = MLP(
+        input_size=state_dim,
+        output_size=action_dim,
+        hidden_sizes=hidden_sizes_actor,
+        output_activation=torch.tanh,
+    ).to(device)
+
+    actor_target = MLP(
+        input_size=state_dim,
+        output_size=action_dim,
+        hidden_sizes=hidden_sizes_actor,
+        output_activation=torch.tanh,
+    ).to(device)
+    actor_target.load_state_dict(actor.state_dict())
+
+    # create critic1
+    critic1 = MLP(
+        input_size=state_dim + action_dim,
+        output_size=1,
+        hidden_sizes=hidden_sizes_critic,
+    ).to(device)
+
+    critic1_target = MLP(
+        input_size=state_dim + action_dim,
+        output_size=1,
+        hidden_sizes=hidden_sizes_critic,
+    ).to(device)
+    critic1_target.load_state_dict(critic1.state_dict())
+
+    # create critic2
+    critic2 = MLP(
+        input_size=state_dim + action_dim,
+        output_size=1,
+        hidden_sizes=hidden_sizes_critic,
+    ).to(device)
+
+    critic2_target = MLP(
+        input_size=state_dim + action_dim,
+        output_size=1,
+        hidden_sizes=hidden_sizes_critic,
+    ).to(device)
+    critic2_target.load_state_dict(critic2.state_dict())
+
+    # concat critic parameters to use one optim
+    critic_parameters = list(critic1.parameters()) + list(critic2.parameters())
+
+    # create optimizer
+    actor_optim = optim.Adam(
+        actor.parameters(),
+        lr=hyper_params["LR_ACTOR"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+
+    critic_optim = optim.Adam(
+        critic_parameters,
+        lr=hyper_params["LR_CRITIC"],
+        weight_decay=hyper_params["WEIGHT_DECAY"],
+    )
+
+    # noise
+    exploration_noise = GaussianNoise(
+        action_dim,
+        min_sigma=hyper_params["EXPLORATION_NOISE"],
+        max_sigma=hyper_params["EXPLORATION_NOISE"],
+    )
+
+    target_policy_noise = GaussianNoise(
+        action_dim,
+        min_sigma=hyper_params["TARGET_POLICY_NOISE"],
+        max_sigma=hyper_params["TARGET_POLICY_NOISE"],
+    )
+
+    # make tuples to create an agent
+    models = (actor, actor_target, critic1, critic1_target, critic2, critic2_target)
+    optims = (actor_optim, critic_optim)
+    noises = (exploration_noise, target_policy_noise)
+
+    # create an agent
+    agent = Agent(env, args, hyper_params, models, optims, noises)
+
+    # run
+    if args.test:
+        agent.test()
+    else:
+        agent.train()