From 1d58bde92f6df5d67255f414506a4cdda8601a1e Mon Sep 17 00:00:00 2001 From: Zheyuan Hu Date: Wed, 24 Aug 2022 00:38:39 -0700 Subject: [PATCH] add hw1 --- README.md | 3 +- hw1/README.md | 77 ++++ hw1/cs285/agents/__init__.py | 0 hw1/cs285/agents/base_agent.py | 17 + hw1/cs285/agents/bc_agent.py | 40 ++ hw1/cs285/expert_data/expert_data_Ant-v4.pkl | Bin 0 -> 1856775 bytes .../expert_data_HalfCheetah-v4.pkl | Bin 0 -> 336775 bytes .../expert_data/expert_data_Hopper-v4.pkl | Bin 0 -> 216775 bytes .../expert_data/expert_data_Humanoid-v4.pkl | Bin 0 -> 6168779 bytes .../expert_data/expert_data_Walker2d-v4.pkl | Bin 0 -> 336775 bytes hw1/cs285/infrastructure/__init__.py | 0 hw1/cs285/infrastructure/colab_utils.py | 26 ++ hw1/cs285/infrastructure/logger.py | 74 +++ hw1/cs285/infrastructure/pytorch_util.py | 75 ++++ hw1/cs285/infrastructure/replay_buffer.py | 89 ++++ hw1/cs285/infrastructure/rl_trainer.py | 269 +++++++++++ hw1/cs285/infrastructure/utils.py | 119 +++++ hw1/cs285/policies/MLP_policy.py | 116 +++++ hw1/cs285/policies/__init__.py | 0 hw1/cs285/policies/base_policy.py | 14 + hw1/cs285/policies/experts/Ant.pkl | Bin 0 -> 151322 bytes hw1/cs285/policies/experts/HalfCheetah.pkl | Bin 0 -> 72593 bytes hw1/cs285/policies/experts/Hopper.pkl | Bin 0 -> 65124 bytes hw1/cs285/policies/experts/Humanoid.pkl | Bin 0 -> 375378 bytes hw1/cs285/policies/experts/Walker2d.pkl | Bin 0 -> 72588 bytes hw1/cs285/policies/loaded_gaussian_policy.py | 105 +++++ hw1/cs285/scripts/run_hw1.ipynb | 425 ++++++++++++++++++ hw1/cs285/scripts/run_hw1.py | 123 +++++ hw1/cs285_hw1.pdf | Bin 0 -> 97122 bytes hw1/installation.md | 41 ++ hw1/requirements.txt | 13 + hw1/requirements_colab.txt | 9 + hw1/setup.py | 8 + 33 files changed, 1641 insertions(+), 2 deletions(-) create mode 100644 hw1/README.md create mode 100644 hw1/cs285/agents/__init__.py create mode 100644 hw1/cs285/agents/base_agent.py create mode 100644 hw1/cs285/agents/bc_agent.py create mode 100644 hw1/cs285/expert_data/expert_data_Ant-v4.pkl create mode 100644 hw1/cs285/expert_data/expert_data_HalfCheetah-v4.pkl create mode 100644 hw1/cs285/expert_data/expert_data_Hopper-v4.pkl create mode 100644 hw1/cs285/expert_data/expert_data_Humanoid-v4.pkl create mode 100644 hw1/cs285/expert_data/expert_data_Walker2d-v4.pkl create mode 100644 hw1/cs285/infrastructure/__init__.py create mode 100644 hw1/cs285/infrastructure/colab_utils.py create mode 100644 hw1/cs285/infrastructure/logger.py create mode 100644 hw1/cs285/infrastructure/pytorch_util.py create mode 100644 hw1/cs285/infrastructure/replay_buffer.py create mode 100644 hw1/cs285/infrastructure/rl_trainer.py create mode 100644 hw1/cs285/infrastructure/utils.py create mode 100644 hw1/cs285/policies/MLP_policy.py create mode 100644 hw1/cs285/policies/__init__.py create mode 100644 hw1/cs285/policies/base_policy.py create mode 100644 hw1/cs285/policies/experts/Ant.pkl create mode 100644 hw1/cs285/policies/experts/HalfCheetah.pkl create mode 100644 hw1/cs285/policies/experts/Hopper.pkl create mode 100644 hw1/cs285/policies/experts/Humanoid.pkl create mode 100644 hw1/cs285/policies/experts/Walker2d.pkl create mode 100644 hw1/cs285/policies/loaded_gaussian_policy.py create mode 100644 hw1/cs285/scripts/run_hw1.ipynb create mode 100644 hw1/cs285/scripts/run_hw1.py create mode 100644 hw1/cs285_hw1.pdf create mode 100644 hw1/installation.md create mode 100644 hw1/requirements.txt create mode 100644 hw1/requirements_colab.txt create mode 100644 hw1/setup.py diff --git a/README.md b/README.md index fb338966b..0a55367d5 100644 --- a/README.md +++ b/README.md @@ -1,2 +1 @@ -# homework_fall2022 -Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2022) +Assignments for [Berkeley CS 285: Deep Reinforcement Learning, Decision Making, and Control](http://rail.eecs.berkeley.edu/deeprlcourse/). \ No newline at end of file diff --git a/hw1/README.md b/hw1/README.md new file mode 100644 index 000000000..83d903ca7 --- /dev/null +++ b/hw1/README.md @@ -0,0 +1,77 @@ +## Setup + +You can run this code on your own machine or on Google Colab. + +1. **Local option:** If you choose to run locally, you will need to install MuJoCo and some Python packages; see [installation.md](installation.md) for instructions. +2. **Colab:** The first few sections of the notebook will install all required dependencies. You can try out the Colab option by clicking the badge below: + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/berkeleydeeprlcourse/homework_fall2021/blob/master/hw1/cs285/scripts/run_hw1.ipynb) + +## Complete the code + +Fill in sections marked with `TODO`. In particular, see + - [infrastructure/rl_trainer.py](cs285/infrastructure/rl_trainer.py) + - [policies/MLP_policy.py](cs285/policies/MLP_policy.py) + - [infrastructure/replay_buffer.py](cs285/infrastructure/replay_buffer.py) + - [infrastructure/utils.py](cs285/infrastructure/utils.py) + - [infrastructure/pytorch_util.py](cs285/infrastructure/pytorch_util.py) + +Look for sections maked with `HW1` to see how the edits you make will be used. +Some other files that you may find relevant + - [scripts/run_hw1.py](cs285/scripts/run_hw1.py) (if running locally) or [scripts/run_hw1.ipynb](cs285/scripts/run_hw1.ipynb) (if running on Colab) + - [agents/bc_agent.py](cs285/agents/bc_agent.py) + +See the homework pdf for more details. + +## Run the code + +Tip: While debugging, you probably want to keep the flag `--video_log_freq -1` which will disable video logging and speed up the experiment. However, feel free to remove it to save videos of your awesome policy! + +If running on Colab, adjust the `#@params` in the `Args` class according to the commmand line arguments above. + +### Section 1 (Behavior Cloning) +Command for problem 1: + +``` +python cs285/scripts/run_hw1.py \ + --expert_policy_file cs285/policies/experts/Ant.pkl \ + --env_name Ant-v2 --exp_name bc_ant --n_iter 1 \ + --expert_data cs285/expert_data/expert_data_Ant-v2.pkl + --video_log_freq -1 +``` + +Make sure to also try another environment. +See the homework PDF for more details on what else you need to run. +To generate videos of the policy, remove the `--video_log_freq -1` flag. + +### Section 2 (DAgger) +Command for section 1: +(Note the `--do_dagger` flag, and the higher value for `n_iter`) + +``` +python cs285/scripts/run_hw1.py \ + --expert_policy_file cs285/policies/experts/Ant.pkl \ + --env_name Ant-v2 --exp_name dagger_ant --n_iter 10 \ + --do_dagger --expert_data cs285/expert_data/expert_data_Ant-v2.pkl \ + --video_log_freq -1 +``` + +Make sure to also try another environment. +See the homework PDF for more details on what else you need to run. + +## Visualization the saved tensorboard event file: + +You can visualize your runs using tensorboard: +``` +tensorboard --logdir data +``` + +You will see scalar summaries as well as videos of your trained policies (in the 'images' tab). + +You can choose to visualize specific runs with a comma-separated list: +``` +tensorboard --logdir data/run1,data/run2,data/run3... +``` + +If running on Colab, you will be using the `%tensorboard` [line magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html) to do the same thing; see the [notebook](cs285/scripts/run_hw1.ipynb) for more details. + diff --git a/hw1/cs285/agents/__init__.py b/hw1/cs285/agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hw1/cs285/agents/base_agent.py b/hw1/cs285/agents/base_agent.py new file mode 100644 index 000000000..d7712a059 --- /dev/null +++ b/hw1/cs285/agents/base_agent.py @@ -0,0 +1,17 @@ + +class BaseAgent(object): + def __init__(self, **kwargs): + super(BaseAgent, self).__init__(**kwargs) + + def train(self) -> dict: + """Return a dictionary of logging information.""" + raise NotImplementedError + + def add_to_replay_buffer(self, paths): + raise NotImplementedError + + def sample(self, batch_size): + raise NotImplementedError + + def save(self, path): + raise NotImplementedError diff --git a/hw1/cs285/agents/bc_agent.py b/hw1/cs285/agents/bc_agent.py new file mode 100644 index 000000000..b7ad366e8 --- /dev/null +++ b/hw1/cs285/agents/bc_agent.py @@ -0,0 +1,40 @@ +from cs285.infrastructure.replay_buffer import ReplayBuffer +from cs285.policies.MLP_policy import MLPPolicySL +from .base_agent import BaseAgent + + +class BCAgent(BaseAgent): + def __init__(self, env, agent_params): + super(BCAgent, self).__init__() + + # init vars + self.env = env + self.agent_params = agent_params + + # actor/policy + self.actor = MLPPolicySL( + self.agent_params['ac_dim'], + self.agent_params['ob_dim'], + self.agent_params['n_layers'], + self.agent_params['size'], + discrete=self.agent_params['discrete'], + learning_rate=self.agent_params['learning_rate'], + ) + + # replay buffer + self.replay_buffer = ReplayBuffer(self.agent_params['max_replay_buffer_size']) + + def train(self, ob_no, ac_na, re_n, next_ob_no, terminal_n): + # training a BC agent refers to updating its actor using + # the given observations and corresponding action labels + log = self.actor.update(ob_no, ac_na) # HW1: you will modify this + return log + + def add_to_replay_buffer(self, paths): + self.replay_buffer.add_rollouts(paths) + + def sample(self, batch_size): + return self.replay_buffer.sample_random_data(batch_size) # HW1: you will modify this + + def save(self, path): + return self.actor.save(path) \ No newline at end of file diff --git a/hw1/cs285/expert_data/expert_data_Ant-v4.pkl 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