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Implement Gymnasium-compliant PPO #319

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Implement Gymnasium-compliant PPO #319

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be6da79
Add Gymnasium and dependencies
dtch1997 Nov 15, 2022
d098870
Implement Gymnasium-compliant PPO script
dtch1997 Nov 15, 2022
aa9ac0c
Ensure pre-commit passes
dtch1997 Nov 15, 2022
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28 changes: 14 additions & 14 deletions cleanrl/ppo_continuous_action.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,8 @@
import time
from distutils.util import strtobool

import gym
import gymnasium as gym
import numpy as np
import pybullet_envs # noqa
import torch
import torch.nn as nn
import torch.optim as optim
Expand Down Expand Up @@ -79,7 +78,10 @@ def parse_args():

def make_env(env_id, seed, idx, capture_video, run_name, gamma):
def thunk():
env = gym.make(env_id)
if capture_video:
env = gym.make(env_id, render_mode="rgb_array")
else:
env = gym.make(env_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
if capture_video:
if idx == 0:
Expand All @@ -89,9 +91,6 @@ def thunk():
env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10))
env = gym.wrappers.NormalizeReward(env, gamma=gamma)
env = gym.wrappers.TransformReward(env, lambda reward: np.clip(reward, -10, 10))
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env

return thunk
Expand Down Expand Up @@ -184,7 +183,8 @@ def get_action_and_value(self, x, action=None):
# TRY NOT TO MODIFY: start the game
global_step = 0
start_time = time.time()
next_obs = torch.Tensor(envs.reset()).to(device)
next_obs, _ = envs.reset(seed=args.seed)
next_obs = torch.Tensor(next_obs).to(device)
next_done = torch.zeros(args.num_envs).to(device)
num_updates = args.total_timesteps // args.batch_size

Expand All @@ -208,16 +208,16 @@ def get_action_and_value(self, x, action=None):
logprobs[step] = logprob

# TRY NOT TO MODIFY: execute the game and log data.
next_obs, reward, done, info = envs.step(action.cpu().numpy())
next_obs, reward, terminated, truncated, infos = envs.step(action.cpu().numpy())
done = terminated or truncated
rewards[step] = torch.tensor(reward).to(device).view(-1)
next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)

for item in info:
if "episode" in item.keys():
print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
writer.add_scalar("charts/episodic_return", item["episode"]["r"], global_step)
writer.add_scalar("charts/episodic_length", item["episode"]["l"], global_step)
break
if "final_info" in infos:
for info in infos["final_info"]:
print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)

# bootstrap value if not done
with torch.no_grad():
Expand Down