858afa8d7e
* run ddpg on Mujoco benchmark RUN BENCHMARKS * autopep8 * fixed all syntax in refactored ddpg * a little bit more refactoring * autopep8 * identity test with ddpg WIP * enable test_identity with ddpg * refactored ddpg RUN BENCHMARKS * autopep8 * include ddpg into style check * fixing tests RUN BENCHMARKS * set default seed to None RUN BENCHMARKS * run tests and benchmarks in separate buildkite steps RUN BENCHMARKS * cleanup pdb usage * flake8 and cleanups * re-enabled all benchmarks in run-benchmarks-new.py * flake8 complaints * deepq model builder compatible with network functions returning single tensor * remove ddpg test with test_discrete_identity * make ppo_metal use make_vec_env instead of make_atari_env * make ppo_metal use make_vec_env instead of make_atari_env * fixed syntax in ppo_metal.run_atari
If you are curious.
Train a Cartpole agent and watch it play once it converges!
Here's a list of commands to run to quickly get a working example:
# Train model and save the results to cartpole_model.pkl
python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5
# Load the model saved in cartpole_model.pkl and visualize the learned policy
python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play
If you wish to apply DQN to solve a problem.
Check out our simple agent trained with one stop shop deepq.learn function.
- baselines/deepq/experiments/train_cartpole.py - train a Cartpole agent.
In particular notice that once deepq.learn finishes training it returns act function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. Complimentary file enjoy_cartpole.py loads and visualizes the learned policy.
If you wish to experiment with the algorithm
Check out the examples
- baselines/deepq/experiments/custom_cartpole.py - Cartpole training with more fine grained control over the internals of DQN algorithm.
- baselines/deepq/defaults.py - settings for training on atari. Run
python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4
to train on Atari Pong (see more in repo-wide README.md)