[Algorithm] Update PPO examples

.github/unittest/linux_examples/scripts/environment.yml

@@ -27,3 +27,5 @@ dependencies:
     - coverage
     - vmas
     - transformers
+    - gym[atari]
+    - gym[accept-rom-license]

.github/unittest/linux_examples/scripts/run_test.sh

@@ -48,18 +48,21 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/decision_trans
 # ==================================================================================== #
 # ================================ Gymnasium ========================================= #
 
-python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo.py \
-  env.num_envs=1 \
-  env.device=cuda:0 \
-  collector.total_frames=48 \
-  collector.frames_per_batch=16 \
-  collector.collector_device=cuda:0 \
-  optim.device=cuda:0 \
+python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo_mujoco.py \
+  env.env_name=HalfCheetah-v4 \
+  collector.total_frames=40 \
+  collector.frames_per_batch=20 \
   loss.mini_batch_size=10 \
   loss.ppo_epochs=1 \
   logger.backend= \
-  logger.log_interval=4 \
-  optim.lr_scheduler=False
+  logger.test_interval=40
+python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo_atari.py \
+  collector.total_frames=80 \
+  collector.frames_per_batch=20 \
+  loss.mini_batch_size=20 \
+  loss.ppo_epochs=1 \
+  logger.backend= \
+  logger.test_interval=40
 python .github/unittest/helpers/coverage_run_parallel.py examples/ddpg/ddpg.py \
   collector.total_frames=48 \
   collector.init_random_frames=10 \
@@ -208,18 +211,6 @@ python .github/unittest/helpers/coverage_run_parallel.py examples/dqn/dqn.py \
   record_video=True \
   record_frames=4 \
   buffer_size=120
-python .github/unittest/helpers/coverage_run_parallel.py examples/ppo/ppo.py \
-  env.num_envs=1 \
-  env.device=cuda:0 \
-  collector.total_frames=48 \
-  collector.frames_per_batch=16 \
-  collector.collector_device=cuda:0 \
-  optim.device=cuda:0 \
-  loss.mini_batch_size=10 \
-  loss.ppo_epochs=1 \
-  logger.backend= \
-  logger.log_interval=4 \
-  optim.lr_scheduler=False
 python .github/unittest/helpers/coverage_run_parallel.py examples/redq/redq.py \
   total_frames=48 \
   init_random_frames=10 \

examples/ppo/README.md

@@ -0,0 +1,29 @@
+## Reproducing Proximal Policy Optimization (PPO) Algorithm Results
+
+This repository contains scripts that enable training agents using the Proximal Policy Optimization (PPO) Algorithm on MuJoCo and Atari environments. We follow the original paper [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) by Schulman et al. (2017) to implement the PPO algorithm but introduce the improvement of computing the Generalised Advantage Estimator (GAE) at every epoch.
+
+
+## Examples Structure
+
+Please note that each example is independent of each other for the sake of simplicity. Each example contains the following files:
+
+1. **Main Script:** The definition of algorithm components and the training loop can be found in the main script  (e.g. ppo_atari.py).
+
+2. **Utils File:** A utility file is provided to contain various helper functions, generally to create the environment and the models (e.g. utils_atari.py).
+
+3. **Configuration File:** This file includes default hyperparameters specified in the original paper. Users can modify these hyperparameters to customize their experiments  (e.g. config_atari.yaml).
+
+
+## Running the Examples
+
+You can execute the PPO algorithm on Atari environments by running the following command:
+
+```bash
+python ppo_atari.py
+```
+
+You can execute the PPO algorithm on MuJoCo environments by running the following command:
+
+```bash
+python ppo_mujoco.py
+``` 

examples/ppo/config_atari.yaml

@@ -0,0 +1,36 @@
+# Environment
+env:
+  env_name: PongNoFrameskip-v4
+  num_envs: 8
+
+# collector
+collector:
+  frames_per_batch: 4096
+  total_frames: 40_000_000
+
+# logger
+logger:
+  backend: wandb
+  exp_name: Atari_Schulman17
+  test_interval: 40_000_000
+  num_test_episodes: 3
+
+# Optim
+optim:
+  lr: 2.5e-4
+  eps: 1.0e-6
+  weight_decay: 0.0
+  max_grad_norm: 0.5
+  anneal_lr: True
+
+# loss
+loss:
+  gamma: 0.99
+  mini_batch_size: 1024
+  ppo_epochs: 3
+  gae_lambda: 0.95
+  clip_epsilon: 0.1
+  anneal_clip_epsilon: True
+  critic_coef: 1.0
+  entropy_coef: 0.01
+  loss_critic_type: l2

examples/ppo/config_mujoco.yaml

@@ -0,0 +1,33 @@
+# task and env
+env:
+  env_name: HalfCheetah-v3
+
+# collector
+collector:
+  frames_per_batch: 2048
+  total_frames: 1_000_000
+
+# logger
+logger:
+  backend: wandb
+  exp_name: Mujoco_Schulman17
+  test_interval: 1_000_000
+  num_test_episodes: 5
+
+# Optim
+optim:
+  lr: 3e-4
+  weight_decay: 0.0
+  anneal_lr: False
+
+# loss
+loss:
+  gamma: 0.99
+  mini_batch_size: 64
+  ppo_epochs: 10
+  gae_lambda: 0.95
+  clip_epsilon: 0.2
+  anneal_clip_epsilon: False
+  critic_coef: 0.25
+  entropy_coef: 0.0
+  loss_critic_type: l2