Code for several RL algorithms used in the following papers:

• "Improving Policy Gradient by Exploring Under-appreciated Rewards" by Ofir Nachum, Mohammad Norouzi, and Dale Schuurmans.
• "Bridging the Gap Between Value and Policy Based Reinforcement Learning" by Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans.
• "Trust-PCL: An Off-Policy Trust Region Method for Continuous Control" by Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans.

Available algorithms:

• Actor Critic
• TRPO
• PCL
• Unified PCL
• Trust-PCL
• PCL + Constraint Trust Region (un-published)
• REINFORCE
• UREX

Requirements:

• TensorFlow (see http://www.tensorflow.org for how to install/upgrade)
• OpenAI Gym (see http://gym.openai.com/docs)
• NumPy (see http://www.numpy.org/)
• SciPy (see http://www.scipy.org/)

Quick Start:

Run UREX on a simple environment:

python trainer.py --logtostderr --batch_size=400 --env=DuplicatedInput-v0 \
  --validation_frequency=25 --tau=0.1 --clip_norm=50 \
  --num_samples=10 --objective=urex

Run REINFORCE on a simple environment:

python trainer.py --logtostderr --batch_size=400 --env=DuplicatedInput-v0 \
  --validation_frequency=25 --tau=0.01 --clip_norm=50 \
  --num_samples=10 --objective=reinforce

Run PCL on a simple environment:

python trainer.py --logtostderr --batch_size=400 --env=DuplicatedInput-v0 \
  --validation_frequency=25 --tau=0.025 --rollout=10 --critic_weight=1.0 \
  --gamma=0.9 --clip_norm=10 --replay_buffer_freq=1 --objective=pcl

Run PCL with expert trajectories on a simple environment:

python trainer.py --logtostderr --batch_size=400 --env=DuplicatedInput-v0 \
  --validation_frequency=25 --tau=0.025 --rollout=10 --critic_weight=1.0 \
  --gamma=0.9 --clip_norm=10 --replay_buffer_freq=1 --objective=pcl \
  --num_expert_paths=10

Run Mujoco task with TRPO:

python trainer.py --logtostderr --batch_size=25 --env=HalfCheetah-v1 \
  --validation_frequency=5 --rollout=10 --gamma=0.995 \
  --max_step=1000 --cutoff_agent=1000 \
  --objective=trpo --norecurrent --internal_dim=64 --trust_region_p \
  --max_divergence=0.05 --value_opt=best_fit --critic_weight=0.0 \

Run Mujoco task with Trust-PCL:

python trainer.py --logtostderr --batch_size=1 --env=HalfCheetah-v1 \
  --validation_frequency=50 --rollout=10 --critic_weight=0.0 \
  --gamma=0.995 --clip_norm=40 --learning_rate=0.002 \
  --replay_buffer_freq=1 --replay_buffer_size=20000 \
  --replay_buffer_alpha=0.1 --norecurrent --objective=pcl \
  --max_step=100 --tau=0.0 --eviction=fifo --max_divergence=0.001 \
  --internal_dim=64 --cutoff_agent=1000 \
  --replay_batch_size=25 --nouse_online_batch --batch_by_steps \
  --sample_from=target --value_opt=grad --value_hidden_layers=2 \
  --update_eps_lambda --unify_episodes --clip_adv=1.0 \
  --target_network_lag=0.99 --prioritize_by=step

Run Mujoco task with PCL constraint trust region:

python trainer.py --logtostderr --batch_size=25 --env=HalfCheetah-v1 \
  --validation_frequency=5 --tau=0.001 --rollout=50 --gamma=0.99 \
  --max_step=1000 --cutoff_agent=1000 \
  --objective=pcl --norecurrent --internal_dim=64 --trust_region_p \
  --max_divergence=0.01 --value_opt=best_fit --critic_weight=0.0 \
  --tau_decay=0.1 --tau_start=0.1

Maintained by Ofir Nachum (ofirnachum).