def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000,
1,
float_obs=True)
self.num_exploration_steps = agent_params['num_exploration_steps']
self.offline_exploitation = agent_params['offline_exploitation']
self.exploitation_critic = CQLCritic(agent_params, self.optimizer_spec)
self.exploration_critic = DQNCritic(agent_params, self.optimizer_spec)
if agent_params['use_cbe']:
self.exploration_model = CountBasedModel(
agent_params['cbe_coefficient'], env)
else:
self.exploration_model = RNDModel(agent_params,
self.optimizer_spec)
self.explore_weight_schedule: Schedule = agent_params[
'explore_weight_schedule']
self.exploit_weight_schedule: Schedule = agent_params[
'exploit_weight_schedule']
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params['exploit_rew_shift']
self.exploit_rew_scale = agent_params['exploit_rew_scale']
self.eps = agent_params['eps']
def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000,
1,
float_obs=True)
self.num_exploration_steps = agent_params['num_exploration_steps']
self.offline_exploitation = agent_params['offline_exploitation']
self.exploitation_critic = CQLCritic(
agent_params,
self.optimizer_spec) # estimates policy return under actual reward
self.exploration_critic = DQNCritic(
agent_params, self.optimizer_spec
) # estimates policy return under reward with exploration bonus
if agent_params['my_exploration']:
self.exploration_model = DistanceModel(agent_params,
self.batch_size)
print('using my exploration model')
else:
self.exploration_model = RNDModel(agent_params,
self.optimizer_spec)
print('using RND model')
self.explore_weight_schedule = agent_params['explore_weight_schedule']
self.exploit_weight_schedule = agent_params['exploit_weight_schedule']
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params['exploit_rew_shift']
self.exploit_rew_scale = agent_params['exploit_rew_scale']
self.eps = agent_params['eps']
def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000, 1, float_obs=True)
self.num_exploration_steps = agent_params['num_exploration_steps']
self.offline_exploitation = agent_params['offline_exploitation']
self.exploitation_critic = CQLCritic(agent_params, self.optimizer_spec)
self.exploration_critic = DQNCritic(agent_params, self.optimizer_spec)
if agent_params['use_pred_error']:
print("EXPLORATION: Using prediction error model")
self.explore_model = "pred_error"
self.exploration_model = PredErrorModel(agent_params, self.optimizer_spec)
else:
self.explore_model = "rnd"
self.exploration_model = RNDModel(agent_params, self.optimizer_spec)
self.explore_weight_schedule = agent_params['explore_weight_schedule']
self.exploit_weight_schedule = agent_params['exploit_weight_schedule']
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params['exploit_rew_shift']
self.exploit_rew_scale = agent_params['exploit_rew_scale']
self.eps = agent_params['eps']
self.t = 0
def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000, 1, float_obs=True)
self.num_exploration_steps = agent_params['num_exploration_steps']
self.offline_exploitation = agent_params['offline_exploitation']
self.exploitation_critic = CQLCritic(agent_params, self.optimizer_spec)
self.exploration_critic = DQNCritic(agent_params, self.optimizer_spec)
if agent_params["use_dynamics"]:
print("Using Dynamics Prediction Model")
self.use_dynamics = True
self.exploration_model = DynamicsModel(agent_params, self.optimizer_spec)
else:
self.exploration_model = RNDModel(agent_params, self.optimizer_spec)
self.explore_weight_schedule = agent_params['explore_weight_schedule']
self.exploit_weight_schedule = agent_params['exploit_weight_schedule']
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params['exploit_rew_shift']
self.exploit_rew_scale = agent_params['exploit_rew_scale']
self.eps = agent_params['eps']
self.gamma = agent_params['gamma']
def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000,
1,
float_obs=True)
self.num_exploration_steps = agent_params["num_exploration_steps"]
self.offline_exploitation = agent_params["offline_exploitation"]
self.exploitation_critic = CQLCritic(agent_params, self.optimizer_spec)
self.exploration_critic = DQNCritic(agent_params, self.optimizer_spec)
self.exploration_model = RNDModel(agent_params, self.optimizer_spec)
self.explore_weight_schedule = agent_params["explore_weight_schedule"]
self.exploit_weight_schedule = agent_params["exploit_weight_schedule"]
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params["exploit_rew_shift"]
self.exploit_rew_scale = agent_params["exploit_rew_scale"]
self.eps = agent_params["eps"]
self.modified_eps_greedy = agent_params["modified_eps_greedy"]
def __init__(self, env, agent_params):
super(ExplorationOrExploitationAgent, self).__init__(env, agent_params)
self.replay_buffer = MemoryOptimizedReplayBuffer(100000,
1,
float_obs=True)
self.num_exploration_steps = agent_params['num_exploration_steps']
self.offline_exploitation = agent_params['offline_exploitation']
self.exploitation_critic = CQLCritic(agent_params, self.optimizer_spec)
self.exploration_critic = DQNCritic(agent_params, self.optimizer_spec)
self.exploration_model = RNDModel(agent_params, self.optimizer_spec)
self.explore_weight_schedule = agent_params['explore_weight_schedule']
self.exploit_weight_schedule = agent_params['exploit_weight_schedule']
self.actor = ArgMaxPolicy(self.exploration_critic)
self.eval_policy = ArgMaxPolicy(self.exploitation_critic)
self.exploit_rew_shift = agent_params['exploit_rew_shift']
self.exploit_rew_scale = agent_params['exploit_rew_scale']
self.eps = agent_params['eps']
self.use_counts = agent_params['use_counts']
self.counts_dict = defaultdict(lambda: 0, dict())